Bitwise https://www.bitwiseglobal.com/en-us/ Technology Consulting and Data Management Services Thu, 29 May 2025 04:40:03 +0000 en-US hourly 1 https://cdn2.bitwiseglobal.com/bwglobalprod-cdn/2022/12/cropped-cropped-bitwise-favicon-32x32.png Bitwise https://www.bitwiseglobal.com/en-us/ 32 32 AI is Revolutionizing Insurance: Here’s How https://www.bitwiseglobal.com/en-us/blog/ai-is-revolutionizing-insurance-heres-how/ https://www.bitwiseglobal.com/en-us/blog/ai-is-revolutionizing-insurance-heres-how/#respond Fri, 09 May 2025 09:33:22 +0000 https://www.bitwiseglobal.com/en-us/?p=50483 Introduction The insurance industry is undergoing a profound transformation, and artificial intelligence (AI) is at the forefront of this change. While the potential of AI has been widely discussed, its real-world impact is already being felt across the insurance landscape. This blog post delves into how AI is revolutionizing key insurance functions, enhancing operational efficiency, ... Read more

The post AI is Revolutionizing Insurance: Here’s How appeared first on Bitwise.

]]>
Introduction

The insurance industry is undergoing a profound transformation, and artificial intelligence (AI) is at the forefront of this change. While the potential of AI has been widely discussed, its real-world impact is already being felt across the insurance landscape. This blog post delves into how AI is revolutionizing key insurance functions, enhancing operational efficiency, and ultimately improving the customer experience.

Insurance: A Data-Driven Industry Embracing AI

Insurance relies heavily on data. From assessing risk and pricing policies to processing claims and detecting fraud, data analysis is crucial. Gen AI, with its ability to analyze vast amounts of structured and unstructured data, is a natural fit for the insurance industry. This adoption is further accelerated by the ongoing cloud migration and data platform modernization efforts within the sector.

With our extensive data expertise (over 25 years of enterprise data management experience) and a deep understanding of the challenges that AI can solve in the insurance sector, Bitwise offers advanced data and AI solutions for insurance to help achieve transformational results. Below are key areas that we’ve identified in working with Fortune 500 insurance customers where Gen AI is making a major impact.

Three Key Areas Where AI is Making a Major Impact

Artificial intelligence is rapidly transforming numerous industries, and the insurance sector is no exception. From streamlining intricate workflows to bolstering security measures, AI is proving to be a powerful catalyst for positive change. This section will delve into three key areas where Gen AI is currently making a significant impact within insurance: claims processing, underwriting, and fraud prevention.

  • Claims Processing: The claims process is a critical touchpoint in the customer journey, but it’s often plagued by inefficiencies and errors. AI is streamlining this process by automating data retrieval and reconciliation from various internal and external sources. This enables faster and more accurate claims adjudication, reducing processing times and improving customer satisfaction. Bitwise offers data insights and analytics solutions to achieve business results by making insurance claims faster and easier using AI and ML techniques for data-driven personalized pricing models.
  • Underwriting: The underwriting process can be lengthy and cumbersome, especially in life insurance. AI is accelerating this process by quickly gathering and analyzing customer information, enabling faster onboarding and more personalized policy offerings. The automated underwriting process opens opportunities to cover a wider range of individuals, including those who may have been previously underserved. We offer legacy data maintenance solutions to diagnose and fix data problems using custom quality checks and data validations to achieve optimal query performance and accuracy.
  • Fraud Prevention: Insurance fraud is a significant challenge, costing billions of dollars annually. AI is enhancing fraud detection by analyzing diverse data sources in real-time, including claims histories, public records, and medical guidelines. This proactive approach helps identify and prevent fraudulent claims more effectively.

AI: Driving a Better Customer Experience

While AI’s immediate impact is often focused on operational improvements, the ultimate beneficiary is the customer. Faster AI-powered claims processing, personalized policies, and reduced fraud contribute to a smoother and more satisfying customer experience. AI is also enabling insurers to deliver more empathetic and efficient service, particularly during emotionally challenging situations like claim filing after the loss of a loved one.

Insurance companies trying to implement Gen AI use cases struggle due to issues like scattered data, long turnaround time and inability to assess data. We offer a proven methodology in our data platform modernization solution for transitioning legacy databases, database objects, data ingestion, orchestration and data visualization to the cloud using automation through each phase of assessment, code conversion and data validation – establishing a data foundation designed for AI success.

In addition to AI, other key trends are shaping the future of insurance:

  • Consumer acceptance of AI is growing: Research indicates increasing comfort with AI-powered tools in insurance, particularly in areas like premium determination. A recent study published in InsurTech Digital illustrates the trend with “59% of UK respondents willing to accept fully AI-driven claims approval if it delivers accuracy and speed.”
  • IoT is gaining traction: The growing use of activity trackers and other IoT devices provides opportunities for personalized insurance offerings and data-driven insights. Bitwise offers solutions to develop mobile applications that speed up processes, such as claims processing, and provide greater convenience, transparency and accessibility for customers.
  • EVs and Sustainability: The shift towards electric vehicles (EVs) necessitates innovation in underwriting and risk assessment.
  • ESG considerations: Environmental, social, and governance (ESG) factors are becoming increasingly important for consumers and investors, influencing insurance product development and investment strategies.
  • Cyber risk management: As cyber threats evolve, robust cybersecurity measures are crucial for protecting sensitive customer data and ensuring business continuity.

Case Study of Solving Real Business Problems with AI

A leading Fortune 500 insurer struggled with inefficient and inaccurate data extraction from diverse unstructured documents, hindering claims processing. Bitwise developed an AI data analyzer solution for streamlined processes on claims platforms that addressed the challenge with an AI-driven solution built on LLMs and Databricks. The result: automation of key data point extraction, achieving 85% accuracy and dramatically accelerating quote turnaround by 90%, showcasing the power of AI in transforming operational efficiency.

Conclusion

By embracing these trends and adopting a strategic approach to technology integration, insurers can unlock new opportunities, enhance customer experiences, and thrive in a rapidly evolving market. The future of insurance is technology-driven, and those who embrace innovation will be best positioned for success. Contact us to learn how technology can transform your insurance business.

The post AI is Revolutionizing Insurance: Here’s How appeared first on Bitwise.

]]>
https://www.bitwiseglobal.com/en-us/blog/ai-is-revolutionizing-insurance-heres-how/feed/ 0
Implementing Fine-Grained Data Access Control: A Complete Guide to GCP Column-Level Policy Tags https://www.bitwiseglobal.com/en-us/blog/implementing-fine-grained-data-access-control-a-complete-guide-to-gcp-column-level-policy-tags/ https://www.bitwiseglobal.com/en-us/blog/implementing-fine-grained-data-access-control-a-complete-guide-to-gcp-column-level-policy-tags/#respond Fri, 28 Mar 2025 05:55:16 +0000 https://www.bitwiseglobal.com/en-us/?p=50231 What you will learn Fundamentals of Fine-Grained Data Access Control Learn how to implement GCP column-level security using policy tags and data masking rules Understand best practices for taxonomies and inheritance structures Discover automated approaches to policy tag management See real-world examples of fine-grained access control The Challenge As organizations scale, implementing data governance becomes ... Read more

The post Implementing Fine-Grained Data Access Control: A Complete Guide to GCP Column-Level Policy Tags appeared first on Bitwise.

]]>
What you will learn
  • Fundamentals of Fine-Grained Data Access Control
  • Learn how to implement GCP column-level security using policy tags and data masking rules
  • Understand best practices for taxonomies and inheritance structures
  • Discover automated approaches to policy tag management
  • See real-world examples of fine-grained access control

The Challenge

As organizations scale, implementing data governance becomes more complicated, especially when cross functional teams from marketing, finance, product and sales work on data initiatives. Increasing use of artificial intelligence, machine learning and now generative AI, makes it even more difficult as legal teams require transparency and scrutiny while data is being accessed by the different teams.

Companies that manage data are facing a big challenge. They need to share important business information with the right people, but they also have to keep sensitive data safe.

This sensitive data includes things like:

  • Personally Identifiable Information (PII): Social Security numbers, tax IDs, addresses, emails, passwords, etc.
  • Financial information: Bank account numbers, financial statements, etc.
  • Medical information: Diagnoses, treatment records, etc.

The old way of controlling who sees what data is too simple. It’s like putting a big lock on a whole table in a library, instead of locking individual books. This can let people see things they shouldn’t.

As companies deal with more and more complicated data, they need a much better way to control who can access what. This is called ‘granular access control’ and it’s becoming essential for keeping data safe.

Here are some statistics from IBM and Verizon’s 2024 data breach reports:

  • The staggering financial impact of data breaches reached a global average of $4.88 million!
  • The most common type of data stolen or compromised was customer PII, at 46%. And it can be used in identity theft and credit card fraud.
  • The majority (around 62%) of data breaches are financially motivated.
  • A significant increase in data breaches compared to previous years.

In my role as a Data Engineer at a leading fintech organization, I encountered significant data governance challenges while managing a petabyte-scale data warehouse. Our team was tasked with implementing comprehensive PII data protection across an extensive data ecosystem comprising over 10,000 tables and 1,000+ ELT processing jobs.

The project presented two critical challenges that required careful consideration and strategic planning:

  • Implementing robust data security measures while ensuring zero disruption to existing data products and maintaining seamless service for our customers.
  • Developing an efficient methodology to discover and classify sensitive data across thousands of tables, followed by implementing appropriate redaction and encryption protocols based on defined sensitivity rules.

The scale and complexity of this undertaking was particularly noteworthy given our active data warehouse environment, which required maintaining business continuity while enhancing security protocols.

The Solution: Column-Level Policy Tags in GCP

What Are Policy Tags?

Policy tags in Google Cloud Platform provide a hierarchical system to define and enforce access controls at the column level. Think of them as intelligent labels that:

  • Define security classifications for data
  • Inherit permissions through a taxonomy structure
  • Integrate with IAM roles and permissions
  • Enable dynamic access control

These policy tags are managed using taxonomies in BigQuery. A Taxonomy in BigQuery acts like a hierarchical container system that organizes your policy tags – think of it as a secure file cabinet where each drawer (category) contains specific folders (policy tags) for different types of sensitive data.

These policy tags are then attached to specific columns in your BigQuery tables to control who can see what data. Dynamic data masking on policy tags allows setting up different masking rules for different roles based on their needs. Such as redaction,nullification or custom user defined function without actual data modified in the table.

For example, a “PII_Taxonomy” might have categories like “High_Sensitivity” containing policy tags for Government IDs and social security numbers, while “Medium_Sensitivity” could contain tags for email addresses and phone numbers.

To solve our challenges, we used policy tags to attach to sensitive data fields and then manage permissions at tag level. This provided us flexibility to implement role based access controls (RBAC) without disrupting any table data, or its end users. See the below flow chart for high level steps.

Business process flow example

With legacy domain understanding and subject matter experts, we defined a list of sensitive data that can be ingested into a data warehouse. We then categorized the list based on compliance and legal terms, as to what are high severity sensitive data fields, low and medium, and their consumption patterns. And used it to create our hierarchical taxonomy structure. See for detailed steps and commands to create taxonomy structure in the implementation guide below.

Then we created a program that identified sensitive data fields and also profiled sample data to confirm its sanity. It also identified what policy tags to attach to a data field. This program gave us a matrix of Table, Column and Policy tag that it needs to be attached.

Then we came up with our final program that actually attached policy tags to tables using bq command line tools such as bq schema to get the latest structure of the table, add policy tags to it and use bq update to attach policy tags to tables in BigQuery.

Because there were 10000+ tables, we released the changes in phases instead of one big bang.

Implementation Guide

Let’s create a taxonomy that categorizes PII sensitive data by severity. Each category can have sub-categories for specific policy tags to be applied to table columns. Refer to the diagram below:

Policy tags
Category tags allow us to manage access control at a higher level, reducing administrative overhead. To maximize effectiveness, define categories that align with your organization’s specific business processes and encompass all forms of sensitive information.
 

Step 1: Create taxonomy with parent Policy tag ‘high’ and its child tag ‘driving_license’ as described in above diagram:

  • Refer this python code from this Jupyter notebook for step by step execution create_taxonomy_and_data_masking
  • After executing the code you should see a taxonomy and policy tag structure as below

Pii Sensitive Taxonomy

  • Repeat the same process to create medium and low category and sub-tag for all required tags.

Step 2: Create data masking rules for policy tag driving_licence

  • Let’s create 2 different masking rules for different teams such as below
  • One for sales team who needs to see only last 4 chars of driving licence
  • Another for analytics team who do not need to see the original value but unique hash for each distinct data value
  • Follow the steps in Jupyter notebook to create these.
  • Once you are done with these you can see the masking rules to your policy tag as below in your policy tag console.

Policy Tag

Step 3: Apply Policy Tags to the Columns with appropriate sensitive data

  • Run bq commands on command-line to attach policy tag to your table
  • Refer commands here – attach_policy_tag_to_column.sh
  • After applying the tag you should be able to see it in Bigquery console table schema

Current Schema

Step 4: Assign IAM Permissions to enable access control, first provide necessary permissions to applicable users.

  • Assign roles/bigquerydatapolicy.maskedReader to your sales user on pii_last_four masking rule
  • Assign roles/bigquerydatapolicy.maskedReader to your analytics user on pii_hash masking rule
  • Assign roles/datacatalog.categoryFineGrainedReader to your users who need to access the raw data
  • Refer set_permissions.sh for gcloud commands and follow notebook

Step 5: Enable Access control

  • If you have data masking rules then this will be automatically enabled, and you cannot disable it. So you must authorize users before enabling masking rules or enforcing access control.
  • If you do not have data masking rules this will be manually enforced from the console as shown below. When you do not have masking rules but enforced access control, users who do not have access to policy tag will get an error if they try to query that field.

Access Control

Step 6: Test data access with different type of users with different roles

  • Sales user with maskedReader role to last 4 would see only last 4 of the driving licenses

Step1

  • Analytics users with maskedReader to hash would see only the hashed version of driving license

Step2

  • Users with FineGrainedReader will be able to access both raw sensitive and non-sensitive data seamlessly

Step3

  • Users without FineGrainedReader or maskedReader role will face error if they select a data column that has a policy tag

Error: Access Denied: BigQuery BigQuery: User has neither fine-grained reader nor masked get permission to get data protected by policy tag “pii-sensitive-taxonomy : driving_license” on column your_dataset.customer_data.customer_driving_license.

  • Users without FineGrainedReader or maskedReader will be still able to access non-sensitive data that is not tagged

Step 6: Implement automated monitoring of policy tag lifecycle and for unauthorized tag removals or modifications, and remediation of potential security gaps.

Results and Benefits

  • Enabled selective data access control at column level, allowing organizations to protect sensitive fields (like tax IDs, credit card numbers) while keeping non-sensitive data (like purchase history) accessible to appropriate users
  • Strengthen regulatory compliance by providing granular control and audit trails for sensitive data access, helping meet both internal policies and external regulations (GDPR, CCPA, etc.)
  • Ensured continuous compliance through automated monitoring of policy tag lifecycle, with real-time alerts for unauthorized tag removals or modifications, enabling prompt remediation of potential security gaps
  • Enhanced customer and partner trust by demonstrating robust protection of their sensitive information through precise, documented data access controls
  • Mitigated security risks by preventing unauthorized access to sensitive columns while maintaining business efficiency, replacing the traditional “all-or-nothing” access approach
  • Improved operational efficiency by allowing data analysts to access necessary non-sensitive data without being blocked by overly broad security restrictions

Use phased approach for large data warehouses

  • Prioritize Business Continuity: Implement changes in a phased manner to avoid significant service interruptions and perform thorough impact analysis of downstream applications and ELT pipelines
  • Identify Stakeholders: Determine all users and service accounts that currently access sensitive data.
  • Assess Data Access Patterns: Analyze existing data access methods, such as SELECT * queries and views, to identify potential impacts.
  • Categorize Access Needs: Classify users, groups, and processes based on their required level of access to sensitive information.
  • Implement Gradual Access Control: Before enabling full access control, grant fine-grained permissions to essential users and service accounts.
  • Communicate Changes: Proactively inform affected teams about the upcoming changes and establish clear escalation procedures for incident reporting

Best Practices & Tips Taxonomy Design

  • Create logical groupings based on sensitivity levels
  • Use meaningful, standardised naming conventions
  • Document taxonomy decisions and rationale
  • Regularly audit policy tag assignments
  • Implement least-privilege access principles
  • Monitor and log access patterns

Conclusion

As organizations continue to navigate the complexities of data governance, implementing column-level security through GCP policy tags represents a significant leap forward in protecting sensitive information while maintaining operational efficiency. Our journey through implementing this solution at petabyte scale demonstrates that even large-scale data warehouses can successfully transition to granular access controls without disrupting business operations.

For organizations looking to enhance their data security posture, GCP’s policy tags offer a robust, scalable solution that aligns with modern data governance requirements. The phased approach we’ve outlined provides a practical roadmap for implementation, whether you’re managing thousands of tables or just beginning your data governance journey.

Contact Us to discuss your data governance needs with our experts and determine if GCP policy tagging and dynamic data masking aligns to your objectives.

What’s Next

For users who have already implemented policy tagging and looking for advanced policy tag management, here are some next steps to think of and apply as needed.

Technical Resources

The post Implementing Fine-Grained Data Access Control: A Complete Guide to GCP Column-Level Policy Tags appeared first on Bitwise.

]]>
https://www.bitwiseglobal.com/en-us/blog/implementing-fine-grained-data-access-control-a-complete-guide-to-gcp-column-level-policy-tags/feed/ 0
Accelerating Time-to-Value with Looker: The Future of BI https://www.bitwiseglobal.com/en-us/blog/accelerating-time-to-value-with-looker-the-future-of-bi/ https://www.bitwiseglobal.com/en-us/blog/accelerating-time-to-value-with-looker-the-future-of-bi/#respond Mon, 10 Mar 2025 05:31:24 +0000 https://www.bitwiseglobal.com/en-us/?p=50104 Introduction The landscape of analytics has undergone a dramatic transformation, driven by the increasing complexity of data and the ever-growing demand for timely insights. Legacy BI tools, once the industry standard, are now struggling to keep pace with the evolving needs of modern businesses. The need to modernize these legacy reports has become imperative to ... Read more

The post Accelerating Time-to-Value with Looker: The Future of BI appeared first on Bitwise.

]]>
Introduction

The landscape of analytics has undergone a dramatic transformation, driven by the increasing complexity of data and the ever-growing demand for timely insights. Legacy BI tools, once the industry standard, are now struggling to keep pace with the evolving needs of modern businesses. The need to modernize these legacy reports has become imperative to unlock the full potential of data-driven decision-making and Generative AI assisted analytics.

Let’s dive into what makes Looker a compelling BI tool and explore modernization challenges when migrating legacy BI from tools like Cognos and Tableau to Looker.

Why Looker for Modern BI

Looker, a powerful data platform offered by Google Cloud, has emerged as a leading choice to modernize BI, especially for companies already using Google BigQuery.

Key Features of Looker for Modern Analytics

Looker offers a unique blend of features that make it an ideal solution for businesses seeking to accelerate time-to-value from their data.

  • Intuitive User Interface: Looker’s user-friendly interface empowers business users to explore data independently, reducing reliance on technical teams.
  • Semantic Layer: This powerful abstraction layer provides a unified view of data across disparate sources, simplifying data exploration and analysis.
  • Advanced Analytics: Looker enables advanced analytics techniques like machine learning and predictive modeling, allowing organizations to uncover deeper insights.
  • Customization and Flexibility: Looker can be tailored to meet the specific needs of any organization, with customizable dashboards, reports, and alerts.
  • Collaboration and Sharing: Seamless collaboration and sharing of insights across teams foster a data-driven culture.
  • AI-Powered Analytics: Gemini in Looker provides AI assisted analytical workflows for more efficient self-service BI.

Why BigQuery Customers Modernize BI Reports in Looker

Specifically for Google BigQuery customers that are using other BI tools, there are many key drivers for modernizing those reports in Looker.

  • LookML provides a re-usable data model that is highly efficient within the BigQuery / GCP ecosystem.
  • Looker and BigQuery deliver faster query performance – up to 10x improved speeds.
  • Looker enables easy integration with BigQuery data sources.
  • Looker excels at complex data modeling, customization of reports, and real-time insights into data.
  • Looker works well with large volumes of data from multiple sources.

Now that we’ve explored why Looker is worth considering for your modern BI needs, let’s take a look at migration challenges.

BI Migration Challenges

Migrating from legacy BI tools to Looker can be a complex process and can present challenges, including issues related to data migration, report and dashboard reconstruction, user adoption, and integration with existing systems.

Common Challenges When Migrating to Looker

Looker offers unique features that require proven solutions and workarounds to achieve similar or better results when migrating from legacy BI tools.

  • Data Model Differences: Each BI platform has different data modelling approaches, leading to discrepancies in data representation and calculations.
  • Excel Migration: Looker does not have direct Excel import, so data needs to be extracted and converted.
  • User Training Needs: Users familiar with their existing BI tools may have difficulty adapting to Looker’s interface, navigation, and functionalities.
  • Data Blending in Looker: Data Blending, a widely used method for combining multiple sources, is not a feature in Looker and requires alternative methods for achieving similar outcomes.
  • Visualization Variances: Every BI tool offers its unique set of visualizations or formatting options, impacting the layout and design of dashboards.
  • Performance Optimization: Large data sets and complex queries can have an impact on performance without proper query optimization.

Tableau Migration to Looker Success Story

With extensive BI Modernization experience, Bitwise understands the differences between legacy tools and modern tools like Looker. We’ve been through the migration process and have developed solutions to address the unique challenges of migrating to Looker with proven success in keeping BI migrations on track and within budget.

For instance, a leading American ticketing company faced the challenge of maintaining and scaling its existing Tableau reports. As their data volume and complexity grew, they sought a modern, scalable solution to streamline their BI operations and unlock deeper insights. Bitwise solution helped the firm in migrating Tableau Report Migration to Looker while reducing the development and maintenance costs.

Recommendations for BI Modernization

To overcome the challenges mentioned above, a structured approach to legacy BI modernization is essential.

Proven BI Modernization Approach

Bitwise recommends the following approach to ensuring a seamless transition from legacy BI to Looker.

  • Assessment: Evaluate the current BI landscape, identify pain points, and define modernization goals.
  • Planning: Develop a detailed migration plan, including data migration strategies, report and dashboard mapping, and user training.
  • Implementation: Migrate data, build reports and dashboards, and configure Looker to meet specific business needs.
  • Testing and Validation: Thoroughly test the migrated environment to ensure data accuracy and report functionality.
  • Deployment and User Training: Deploy Looker to end-users and provide comprehensive training to maximize adoption.
  • Ongoing Support and Optimization: Continuously monitor and optimize the Looker environment to ensure optimal performance and user satisfaction.

Access our whitepaper on modernizing BI and transforming data into actionable insights to know more about a strategic approach to modernizing your legacy BI environments to become truly data driven.

Achieving BI Modernization Success with Looker

Using the above approach, Bitwise helped a global financial services company that was struggling with a legacy WebFocus-based reporting and visualization application. The company was plagued by high maintenance costs, performance issues, and poor user experience.

To address these challenges, they partnered with Bitwise to modernize legacy reporting on the cloud with Looker. By leveraging Looker’s powerful analytics capabilities, the company significantly improved report performance, reduced costs, and enhanced user experience. The intuitive interface and self-service capabilities empowered users to access real-time data and generate insights more efficiently, driving data-driven decision-making accelerating business growth.

Getting Started

While there are many challenges to modernizing BI in Looker, the benefits can outweigh the challenges and set your organization up for a modern, data-driven future. In addition to using a proven migration approach, Bitwise, a trusted Google Cloud Partner and leading provider of data solutions, offers a range of accelerators and value-adds to expedite the BI modernization process.

Our proven BI modernization methodology will guide you through the entire process, from assessment to deployment. Contact us today to start your BI Modernization journey and accelerate time-to-value with Looker.

The post Accelerating Time-to-Value with Looker: The Future of BI appeared first on Bitwise.

]]>
https://www.bitwiseglobal.com/en-us/blog/accelerating-time-to-value-with-looker-the-future-of-bi/feed/ 0
Achieve Microsoft Fabric Readiness with Bitwise https://www.bitwiseglobal.com/en-us/blog/achieve-microsoft-fabric-readiness-with-bitwise/ https://www.bitwiseglobal.com/en-us/blog/achieve-microsoft-fabric-readiness-with-bitwise/#respond Fri, 24 Jan 2025 09:36:36 +0000 https://www.bitwiseglobal.com/en-us/?p=49951 Introduction In today’s data-driven landscape, organizations grapple with the complexities of managing and leveraging their ever-expanding data volumes. Siloed data, legacy systems, and the inability to harness artificial intelligence (AI) pose significant challenges to business agility and innovation. As research shows, only 32% of organizations reported being able to realize tangible and measurable value from ... Read more

The post Achieve Microsoft Fabric Readiness with Bitwise appeared first on Bitwise.

]]>
Introduction

In today’s data-driven landscape, organizations grapple with the complexities of managing and leveraging their ever-expanding data volumes. Siloed data, legacy systems, and the inability to harness artificial intelligence (AI) pose significant challenges to business agility and innovation. As research shows, only 32% of organizations reported being able to realize tangible and measurable value from data.

Modernizing existing analytics solutions with a platform like Microsoft Fabric, an AI-powered analytics platform that unifies data and services, empowers organizations to optimize data management practices and unlock greater value from their information.

A recent Harvard Business Review study underscored the impact of self-service analytics, revealing a 72% productivity boost among organizations that equipped their teams with such tools. Bitwise, with its deep expertise in ETL and data modernization, it is uniquely positioned to assist organizations in their journey to adopting Microsoft Fabric for self-service analytics and AI-driven initiatives.

Understanding Microsoft Fabric

Microsoft Fabric is a unified data platform designed to streamline data management and analysis. It serves as a central hub integrating various Azure data and analytics services, including Data Factory, Azure Synapse Analytics, Synapse Data Engineering, Synapse Data Science, Synapse Data Warehousing and Power BI. By consolidating data and automating management processes, Fabric enables businesses to modernize their data stack and drive faster innovation.

A data warehouse modernization strategy centered around Microsoft Fabric can unlock significant value by unifying data, automating workflows, and facilitating advanced analytics. However, migrating legacy data warehouses to Fabric requires careful planning and execution, especially when it comes to ETL processes.

The Importance of ETL Migration

ETL (Extract, Transform, Load) processes form the backbone of any data warehouse. As organizations embark on data modernization journeys, migrating legacy ETL workflows to a cloud-native platform like Microsoft Fabric is essential. This migration not only enhances data quality and consistency but also unlocks the potential for automation, scalability, and integration with advanced analytics capabilities.

Choosing the Right Migration Path

Microsoft Fabric offers several ETL options, including Microsoft Fabric Notebooks (with PySpark, Scala, Spark SQL, Python, etc.), Microsoft Fabric Spark Job Definition, Data Pipelines and Fabric Dataflows Gen 2. Selecting the optimal path depends on various factors, such as team expertise, data volume, complexity, and performance requirements.

Bitwise’s ETL modernization practice excels in assessing client-specific needs and recommending the most suitable migration target. Our automated ETL migration tool supports a wide range of legacy ETL platforms, including SSIS, DataStage, Informatica, Ab Initio, and SQL stored procedures.

Overcoming Migration Challenges

Migrating ETL processes can present challenges, such as data quality issues, performance bottlenecks, and dependency management. Bitwise’s experienced team and advanced tools address these hurdles effectively. Our automated migration solutions accelerate the process while ensuring data accuracy and integrity. Additionally, we provide comprehensive support throughout the migration journey, from planning and assessment to testing and deployment.

Bitwise has a proven track record of successful ETL migrations, including a project for a multinational manufacturer that involved migrating SSIS packages to Azure Data Factory + Dataflow. This project resulted in significant performance improvements and cost reductions. These prior experiences provide a strong foundation for designing and implementing a robust, scalable, and efficient data strategy.

Bitwise and Microsoft

Bitwise’s partnership with Microsoft and our early involvement in the Microsoft Fabric development roadmap enable us to deliver cutting-edge solutions. Our team comprises certified experts who stay up to date with the latest platform advancements.

By choosing Bitwise, organizations gain access to a proven partner with a strong track record in ETL migration and data modernization. Our solutions help clients reduce costs, increase efficiency, accelerate time-to-insights, and improve agility and scalability.

Getting Started

Data modernization is imperative for organizations seeking to thrive in today’s competitive landscape. Microsoft Fabric offers a powerful platform for transforming data management and analytics. Bitwise’s expertise in ETL migration and data modernization can be your trusted ally in this journey. Our 4-week Microsoft Fabric Readiness Assessment deep dive helps you navigate your data modernization journey. Get a tailored roadmap for your data modernization journey on Azure Marketplace.

By working with Bitwise, you can overcome migration challenges, optimize your data infrastructure, and unlock the full potential of your data. Contact us today to discuss your data modernization goals and explore how we can help you achieve them.

The post Achieve Microsoft Fabric Readiness with Bitwise appeared first on Bitwise.

]]>
https://www.bitwiseglobal.com/en-us/blog/achieve-microsoft-fabric-readiness-with-bitwise/feed/ 0
Microsoft Ignite 2024: AI, Cloud Innovation & Microsoft Fabric for Business Transformation https://www.bitwiseglobal.com/en-us/blog/microsoft-ignite-2024-ai-cloud-innovation-microsoft-fabric-for-business-transformation/ https://www.bitwiseglobal.com/en-us/blog/microsoft-ignite-2024-ai-cloud-innovation-microsoft-fabric-for-business-transformation/#respond Mon, 23 Dec 2024 07:31:48 +0000 https://www.bitwiseglobal.com/en-us/?p=49891 Introduction Microsoft Ignite Conference was a whirlwind of innovation, unveiling new technologies and strategies that will reshape the future of business. From AI-powered solutions to groundbreaking cloud innovations, Microsoft Ignite 2024 sessions showcased a vision of a digitally transformed world. As per Microsoft, companies worldwide are investing many resources in integrating Gen AI into their ... Read more

The post Microsoft Ignite 2024: AI, Cloud Innovation & Microsoft Fabric for Business Transformation appeared first on Bitwise.

]]>
Introduction

Microsoft Ignite Conference was a whirlwind of innovation, unveiling new technologies and strategies that will reshape the future of business. From AI-powered solutions to groundbreaking cloud innovations, Microsoft Ignite 2024 sessions showcased a vision of a digitally transformed world. As per Microsoft, companies worldwide are investing many resources in integrating Gen AI into their workstreams to benefit their clients and employees. Microsoft’s internal data and third-party research also indicate the ongoing AI boom as over 85% of Fortune 500 organizations are using Microsoft AI and 70% are using Microsoft 365 CoPilot.

In the Microsoft Ignite 2024 Keynotes, CEO Satya Nadella introduced around 80 ways in which Copilot and AI can assist startups in successfully building and implementing AI-powered solutions for delivering value to their clients. In this blog post, we’ll delve into key takeaways from the event and explore how Bitwise can help you leverage these advancements and accelerate your data modernization journey.

Key Takeaways from Microsoft Ignite 2024

Microsoft Ignite Announcements highlighted how nearly 70% of Fortune 500 companies are now using Microsoft 365 Copilot—it’s clear that AI is transforming industries at every level with the use of generative AI on the rise. Organizations and startups must balance the adoption of AI securely and responsibly. Microsoft Ignite 2024 Highlights covers the key focus of this year’s AI and Microsoft innovations making it accessible and impactful around different industries. The major themes from the keynote include:

1. AI-enabled Business Users: A New Era of Innovation

The impact of AI is evident in the return on investment for organizations. According to a Microsoft survey, each dollar invested in generative AI yielded a 3.7x return. In the sessions, Microsoft launched sustainable AI initiatives in various sectors, including financial services, public sector, retail, and manufacturing. These initiatives are designed to handle diverse tasks, freeing workers from mundane duties.

Microsoft also addressed long-awaited changes, such as reducing the number of items without CI/CD support, and ensuring that by the end of the year, these challenges will be resolved.

  • AI-First Approach: Microsoft emphasized its commitment to AI, integrating it into various products and services. Companies with AI-powered agents can assess vast amounts of data, execute complex procedures, and adapt to changing environments. Whether it’s automating customer service interactions, enhancing personalized shopping experiences, or driving sustainability efforts, AI has the potential to revolutionize industries.
  • Copilot as a Universal Assistant: Copilot, Microsoft’s AI-powered assistant, was showcased as a powerful tool for productivity and creativity, across various Microsoft 365 apps.
  • AI-Driven Insights: Advanced AI capabilities were demonstrated to extract valuable insights from data, enabling data-driven decision-making.

2. Cloud Innovation Redefined

Microsoft announced a significant expansion of its database capabilities on Azure. Azure SQL Database is now generally available as a source for mirroring, and SQL Managed Instance is entering the preview phase for this feature. [Note: if you have on-premise SQL Server databases, check out this SQL server migration case study to see how Bitwise helps migrate to Azure SQL MI.]

Additionally, Microsoft has introduced the concept of Open Mirroring, which eliminates the need for specific technologies to implement mirroring within the Fabric platform. This flexibility allows for seamless data synchronization between applications and Fabric, enabling a broader range of solutions to be integrated into a unified platform.

  • Azure Innovations: Microsoft announced significant advancements in Azure, including new services and features to enhance performance, security, and scalability.
  • Hybrid and Multi-Cloud Solutions: Microsoft reinforced its commitment to hybrid and multi-cloud strategies, providing flexible solutions for businesses of all sizes.
  • Edge Computing and IoT: Microsoft highlighted the importance of edge computing and IoT, enabling real-time insights and intelligent devices.

3. Microsoft Fabric – AI-powered Data Platform

Microsoft introduced a new database class within the Microsoft Fabric platform, initially incorporating SQL databases. This innovation allows applications to be positioned closer to analytical solutions when both reside within the same environment. By automating data replication to OneLake in a delta format, the platform ensures a seamless and near-real-time synchronization of data. This approach provides a unified SQL endpoint for both transactional and analytical workloads, significantly reducing latency and enhancing overall performance. This groundbreaking development has the potential to reshape future architectural patterns.

  • Real-time Intelligence: Simplifies working with real-time data like sensor data or application logs.
  • Fabric Databases: Integrates the entire Microsoft SQL Server database portfolio into Fabric. Offers full transactional capabilities and seamless data integration with OneLake.
  • Industry Solutions: Pre-built solutions for specific industries like sustainability, healthcare, and retail to accelerate time to value.
  • Fabric Workload Development Kit: Allows developers to extend Fabric with custom workloads and integrate their own ISV solutions.
  • Open and AI-Ready Data Lake: Focuses on OneLake, a scalable and globally deployed data lake with open data formats for easy access and analysis.
  • Shortcuts: Virtualize data connections across various storage systems (cloud/on-prem) for a unified view in OneLake.
  • Mirroring: Continuously replicates data from operational databases into OneLake for seamless data access.

Check out Unlock Your Analytics Potential with Microsoft Fabric Webinar for more insights.

How Bitwise Can Help

At Bitwise, we understand the transformative power of technology. We’re excited about the innovations unveiled at Microsoft Ignite 2024 and being a Microsoft partner, we are committed to helping you leverage these advancements to achieve your business goals.

Bitwise can help you accelerate your data modernization journey by:

  • Modernizing Legacy Data Platforms: Migrating your legacy data platforms to the cloud to improve performance, scalability, and security.
  • Optimizing Data Pipelines: Designing and implementing efficient data pipelines to extract valuable insights from your data.
  • Leveraging AI and Machine Learning: Implementing AI and machine learning solutions to automate tasks, improve decision-making, and drive innovation.
  • Adopting Microsoft Fabric: Helping you adopt Microsoft Fabric to unify your data, streamline analytics, and accelerate time to insight.

In fact, earlier this year we launched our Microsoft Fabric Readiness Program to help clients plan and implement the steps needed to modernize their data for the AI era that we’re now living in.

Conclusion

Microsoft Ignite 2024 has set the stage for a future driven by AI, cloud innovation, and developer empowerment. The evolution of Microsoft Fabric, the integration of AI-powered features, and the enhancements to Power BI are set to make data more accessible, secure, and impactful. By embracing these trends and partnering with Bitwise, you can take advantage of these innovative capabilities by accelerating data modernization to get ready for Microsoft Fabric faster. We’re here to help you unlock the full potential of your data by modernizing your data to the cloud.

The post Microsoft Ignite 2024: AI, Cloud Innovation & Microsoft Fabric for Business Transformation appeared first on Bitwise.

]]>
https://www.bitwiseglobal.com/en-us/blog/microsoft-ignite-2024-ai-cloud-innovation-microsoft-fabric-for-business-transformation/feed/ 0
5 Essential Steps to Assess Your Readiness for Microsoft Fabric Adoption https://www.bitwiseglobal.com/en-us/blog/5-essential-steps-to-assess-your-readiness-for-microsoft-fabric-adoption/ https://www.bitwiseglobal.com/en-us/blog/5-essential-steps-to-assess-your-readiness-for-microsoft-fabric-adoption/#respond Mon, 18 Nov 2024 07:14:50 +0000 https://www.bitwiseglobal.com/en-us/?p=49788 Overview Microsoft Fabric is a revolutionary data management and analytics platform designed to unlock the full potential of your data. By offering a unified approach to data ingestion, preparation, governance, and analysis, the unified AI-powered data analytics platform empowers businesses to gain deeper insights and make data-driven decisions faster. But before diving into Microsoft Fabric, ... Read more

The post 5 Essential Steps to Assess Your Readiness for Microsoft Fabric Adoption appeared first on Bitwise.

]]>
Overview

Microsoft Fabric is a revolutionary data management and analytics platform designed to unlock the full potential of your data. By offering a unified approach to data ingestion, preparation, governance, and analysis, the unified AI-powered data analytics platform empowers businesses to gain deeper insights and make data-driven decisions faster.

But before diving into Microsoft Fabric, it’s crucial to assess your organization’s readiness to ensure a smooth and successful adoption.

This blog post outlines 5 key steps to help you evaluate your current data landscape and determine your organization’s readiness to leverage the Microsoft data fabric platform for your advanced analytics and AI use cases.

1. Analyze Current Data Environment

The first step is to understand your existing data ecosystem. This involves mapping your data sources, formats, volumes, and how they flow through your current ETL (Extract, Transform, Load) processes. Identifying data quality issues, inconsistencies, and siloes is also crucial. By taking a comprehensive inventory of your data environment, you can gain valuable insights into potential challenges and opportunities Microsoft Fabric can address.

2. Define Business Needs and Objectives

What are your key business objectives for leveraging data? Are you looking to improve reporting and analytics, drive data-driven decision-making, or unlock actionable insights from your data sources? Clearly defining your business needs and objectives helps you determine how Microsoft unified analytics platform can contribute to achieving your strategic goals.

3. Assess Technical Readiness

Technical readiness involves evaluating your infrastructure, existing data tools, and team skillsets to determine if they align with Microsoft Fabric’s capabilities. Here are some key considerations:

  • Infrastructure: Does your current infrastructure support migrating to the cloud?
  • Data tools: Are your current tools compatible with Fabric’s services, like Azure Data Factory (ADF) or Azure Databricks?
  • Team skills: Does your team have the necessary skills and expertise to work with Fabric technologies?

4. Develop Migration Planning and Execution

A well-defined migration plan is critical for transitioning your data workloads to Microsoft Fabric. This includes:

  • Prioritization: Identifying the most critical data pipelines to migrate first.
  • Resource allocation: Planning for the personnel and resources necessary to execute the migration.
  • Risk assessment: Anticipating potential challenges and developing mitigation strategies.

For a deeper dive into our proven methodology for migrating ETL jobs to the Microsoft unified analytics platform, check out our recorded webinar Microsoft Fabric: Is Your Business Ready?.

5. Provide Ongoing Optimization

Microsoft Fabric adoption is an ongoing process. After migration, continuous monitoring and optimization are crucial to ensure your data platform operates at peak performance. This involves regularly reviewing data quality, identifying inefficiencies, and implementing optimizations to maximize the value derived from your data.

Fabric Readiness Workshop: Get Ready for Success

As a Microsoft Solutions Partner working closely with the Microsoft Fabric product team to strengthen our capability to migrate data, ETL, and BI workloads to Microsoft Fabric, Bitwise is excited to be a key part of our customers’ transformations to unlock their full potential of Microsoft Fabric. Elevate your data strategy to make sure your organization is prepared for Microsoft Fabric. Enroll in our exclusive Microsoft Fabric Readiness Workshop now! This 3-hour workshop provides valuable insights into Microsoft Fabric’s capabilities and helps you develop a customized roadmap for successful implementation.

FabCon: A Glimpse into the Future of Data Analytics

Microsoft Fabric Community Conference (FabCon) Europe that took place in Stockholm earlier this year was a valuable event for anyone interested in Microsoft Fabric. The key announcements highlighted the platform’s continued evolution and its ability to meet the growing demands of modern data management. Read our blog to learn more about the 3 Key Microsoft Fabric Announcements from FabCon Europe.

Don’t miss the upcoming Microsoft Fabric Community Conference in Las Vegas, Nevada from March 31st to April 2nd, 2025, with workshops happening on March 29th, 30th, and April 3rd. This year’s conference promises an exciting lineup of speakers, informative sessions, and valuable networking opportunities to help you attain the full potential of Microsoft Fabric. Register now for Microsoft Fabric Community Conference 2025 and secure your spot.

The post 5 Essential Steps to Assess Your Readiness for Microsoft Fabric Adoption appeared first on Bitwise.

]]>
https://www.bitwiseglobal.com/en-us/blog/5-essential-steps-to-assess-your-readiness-for-microsoft-fabric-adoption/feed/ 0
3 Key Microsoft Fabric Announcements from FabCon Europe   https://www.bitwiseglobal.com/en-us/blog/3-key-microsoft-fabric-announcements-from-fabcon-europe/ https://www.bitwiseglobal.com/en-us/blog/3-key-microsoft-fabric-announcements-from-fabcon-europe/#respond Thu, 24 Oct 2024 10:23:28 +0000 https://www.bitwiseglobal.com/en-us/?p=49635 European Microsoft Fabric Community Conference Overview FabCon Europe in Stockholm this September was a resounding success, bringing together Microsoft Fabric enthusiasts and partners from across the continent. The event showcased the latest advancements in the platform and provided valuable insights for organizations looking to leverage Fabric for their data initiatives. In this blog post, we’ll ... Read more

The post 3 Key Microsoft Fabric Announcements from FabCon Europe   appeared first on Bitwise.

]]>
European Microsoft Fabric Community Conference Overview

FabCon Europe in Stockholm this September was a resounding success, bringing together Microsoft Fabric enthusiasts and partners from across the continent. The event showcased the latest advancements in the platform and provided valuable insights for organizations looking to leverage Fabric for their data initiatives.

In this blog post, we’ll recap some of the key Microsoft Fabric announcements from FabCon Europe and highlight Bitwise’s Microsoft Fabric Readiness Program, designed to help organizations accelerate their adoption of this powerful data analytics platform.

3 Key Microsoft Fabric Announcements

1. Enhancements for Fabric Data Factory Pipelines

One of the most exciting announcements at FabCon Europe was the introduction of new enhancements for Fabric Data Factory pipelines. These enhancements aim to streamline data integration and transformation processes, making it easier for organizations to build and manage complex data pipelines. Some of the key features include:

  • Improved performance: Enhanced pipeline execution speed and resource utilization.
  • Enhanced monitoring and diagnostics: Improved tools for troubleshooting and optimizing pipeline performance.
  • Expanded data source support: Support for additional data sources and formats.

2. Private Preview of Microsoft Fabric Capacity Calculator

Microsoft also announced the private preview of the Microsoft Fabric SKU Calculator that helps organizations estimate the required compute and storage resources for their Fabric workloads, ensuring they have the right capacity to meet their needs.

The calculator is designed to simplify the process of provisioning Fabric resources and optimize costs. With a straightforward interface, the calculator provides outputs on recommended Fabric SKUs for the infrastructure based on inputs.

3. DP-700 Exam for Data Engineering Using Fabric

For individuals looking to validate their skills in data engineering using Microsoft Fabric, Microsoft introduced the DP-700 exam. This beta exam, available from late October 2024, covers a wide range of topics related to data integration, transformation, warehousing, governance, and Fabric administration.

By passing the DP-700 exam, individuals can demonstrate their expertise in implementing data engineering solutions using Microsoft Fabric and open up new career opportunities, especially as data engineering skills become essential in the era of AI.

Microsoft Fabric Readiness Program from Bitwise

Bitwise is committed to helping organizations maximize the value of Microsoft Fabric. Our Microsoft Fabric Readiness Program offers a comprehensive suite of services designed to accelerate your adoption of the platform. These services include:

  • Assessment and planning: We conduct a thorough assessment of your current data landscape and develop a tailored roadmap for Fabric adoption
  • Migration and modernization: We assist with migrating your existing data workloads to Fabric and modernizing your data architecture.
  • Training and enablement: We provide training and support to help your team become proficient in using Fabric.
  • Ongoing support: We offer ongoing support to ensure the success of your Fabric implementation.

By partnering with Bitwise, you can benefit from our deep expertise in Fabric and our proven track record of delivering successful implementations. Discover how Microsoft Fabric can revolutionize your organization’s data management and analysis capabilities.

See You at FabCon in 2025

FabCon Europe was a valuable event for anyone interested in Microsoft Fabric. The key announcements highlighted the platform’s continued evolution and its ability to meet the growing demands of modern data management.

As a Microsoft Solutions Partner working closely with the Fabric product team to strengthen our capability to migrate data, ETL and BI workloads to Fabric, Bitwise is excited to be a key part of our customer’s transformation to unlock the full potential of Fabric.

Stay tuned to get more updates from Microsoft Fabric Community Conference and we look forward to seeing you in Las Vegas or Vienna in 2025!

The post 3 Key Microsoft Fabric Announcements from FabCon Europe   appeared first on Bitwise.

]]>
https://www.bitwiseglobal.com/en-us/blog/3-key-microsoft-fabric-announcements-from-fabcon-europe/feed/ 0
The Legacy ETL Dilemma – Part 2: A Step-by-Step Guide to Modernize Your ETL Process https://www.bitwiseglobal.com/en-us/blog/the-legacy-etl-dilemma-part-2-a-step-by-step-guide-to-modernize-your-etl-process/ Wed, 09 Oct 2024 11:24:00 +0000 https://www.bitwiseglobal.com/en-us/?p=49460 Introduction If you want to stay ahead of the game in today’s data-driven world, upgrading your ETL process is a must. We know, it might sound scary but breaking it down into simple steps can make it a lot easier. In this guide, we’ll show you how to smoothly move your ETL (Extract, Transform, Load) ... Read more

The post The Legacy ETL Dilemma – Part 2: A Step-by-Step Guide to Modernize Your ETL Process appeared first on Bitwise.

]]>
Introduction

If you want to stay ahead of the game in today’s data-driven world, upgrading your ETL process is a must. We know, it might sound scary but breaking it down into simple steps can make it a lot easier. In this guide, we’ll show you how to smoothly move your ETL (Extract, Transform, Load) process to a modern, cloud-based platform.

In Part 1: Why Modernize Your ETL in the Cloud, we talked about the problems with legacy ETL systems and why it’s important for you to update them. These old systems were built for a different time, and they’re struggling to keep up with the demands of today’s data.

Luckily, cloud-based ETL solutions are a much better fit for your organizational needs. They’re faster, more flexible, and can help you get more out of your data. By the end of this blog, you’ll have a clear plan for upgrading your data management, making things more efficient, and setting your business up for success. Modernizing your ETL might seem like a big project, but it doesn’t have to be complicated. We’ll break it down into 5 steps that will make the process easier for your modernization journey. This blog will discuss each step given below in detail.

  • Step 1: Assessment of Existing Systems
  • Step 2: Selection of Data Platform/ETL Tool Cloud Service
  • Step 3: EDW and Data Migration on Modern Platforms
  • Step 4: ETL Migration Process
  • Step 5: Testing, Monitoring and Cutover

Step1: Assessment of Existing Systems

The first step in ETL modernization is a thorough assessment of your existing system. This involves a thorough assessment of the existing system that should be conducted to identify various aspects including:

  • All data sources and targets
  • Complexity of ETL jobs
  • Data lineage and flow at both orchestration and ETL process levels
  • Batch/jobs execution frequency like hourly, daily, weekly, etc.
  • Existing parameterization frameworks
  • Complexity of data source layouts
  • Data volume, SLAs, and priorities for each batch
  • Usage of any specialized ETL tool features and their occurrences
  • Presence of junk and dead code
  • Utilization of customized scripts in languages such as VB, Unix, Python, Perl, or stored procedures within the ETL process
  • Patterns in ETL jobs to design a more generic process
  • Processes suitable for lift-and-shift versus those requiring redesign in the new environment
  • Analysis on the warehouse objects such as tables, views, stored procedures, constraints, indexes, sequences, etc.
  • Data Profiling and Quality Assessment
  • Compliances in the existing systems

A comprehensive assessment of the existing system is crucial to prevent future surprises and address potential issues related to your design and architecture of modern platforms.

Step 2: Choosing the Right Cloud Platform for ETL Transformation

Based on data collected from the assessment of the existing system, we need to identify the automated ETL migration service that can be best suited for your organization. As we all know, one size does not fit all so given below are the key considerations for you while selecting the right cloud platform:

  • Feature Gap: Assess the differences between the existing ETL tool and the new cloud-based service.
  • Identify Cloud Storage for EDW: For a seamless and efficient migration of your Enterprise Data Warehouse modernization (EDW) from on-premises to the cloud, focus on key factors such as current architecture, data governance, cost-effectiveness, scalability, advanced data modernization methods, robust integration capabilities, disaster. This holistic approach ensures a successful transition and maximizes the benefits of cloud technology.
  • Designing the Target Data Architecture: Design the target data model based on business requirements and the capabilities of the modern platform. Additionally, create a mapping document that aligns the source data schema with the target schema. This document will be used to design the ETL process for loading the EDW.
  • Data Migration Strategy: Based on the data volume, plan the migration approach in phases. Select appropriate data replication tools to periodically refresh data in the newly designed EDW. For high daily data volumes, ensure a CDC-based replication process is in place to avoid moving large data chunks periodically.
  • Feasibility Study: Conduct a detailed feasibility study, supported by multiple POCs, to effectively test the migration plan for database objects and data to modern cloud-based data lakes or delta lakes.
  • Integration Capabilities: Evaluate the ability of ETL service to connect with required data sources and cloud storage accounts.
  • Cost and Performance: Ensure the tool meets the cost and performance requirements to adhere to existing SLAs.
  • Workarounds: Plan for managing tasks and actions currently handled by custom scripts in the existing systems.
  • Generic Capabilities: Check if the tool can implement and manage processes based on patterns identified during the assessment.
  • Compatibility with Modern Practices: Ensure the tool supports future needs, including AI and machine learning use cases.
  • Orchestration Capabilities: Check on native orchestration capabilities and decide if there is a need to go for external third-party schedulers such as Control-M, Tivoli etc.
  • Cloud based: A feasibility check needs to be performed for identification of proper storage accounts to host EDW in cloud platform.
  • Architectural Solutioning: Design a solution that meets both current and future organizational needs.
  • Availability of Skilled Resources: Assess the availability of in-house expertise to manage and support the new system.
  • Proof-of-Concept (POC): A POC driven approach should be taken end to end, with few existing ETL processes to EDW migration to validate all the above parameters for selecting the best suited cloud-based platform and ETL service.

There are a variety of cloud-native ETL services in the market provided by the hyperscalers as well as data integration vendors. Many of these options run on PySpark, which provides flexibility to execute ETL jobs across multiple platforms. Check out ETL Modernization with PySpark to explore further.

Step 3: EDW and Data Migration on Modern Platforms

At this point, if all the above steps have been followed, the migration plan for moving the EDW and data to the modern platform should be ready. Below are a couple extra steps for you which should be considered:

  • Data Governance and Compliance: This data will be used by your developers to test the ETL process. Hence data governance is a curtail step, it involves establishing policies and procedures to ensure data quality, security, and compliance throughout the migration process. Identify and ensure that all necessary data, including PII that falls under various compliance regulations is properly masked.
  • Data Volume: The data replicated in the modern cloud-based data lake should match production volumes to effectively test the performance of the ETL process.

Step 4: ETL Migration Process

During this process, we develop a new set of ETL jobs, processes and batches to load data into cloud-based modern data lakes. The process includes the following steps:

  • Development of Cloud Frameworks: Cloud-native tools introduce a set of principles and best practices different from legacy ETL tools. Hence, development of reusable frameworks is necessary for operations like Data Replication, Parametrization, Notifications, etc. which are compatible with cloud platforms.
  • Develop Generic ETL/Process:Based on the patterns identified during the assessment, developing a generic ETL process significantly reduces code redundancy and effort throughout the overall development process.
  • Lift and Shift Migration: Here those jobs/processes which suits apple to apple conversion are migrated.
  • Redesign/Refactoring: It is necessary to redesign and develop new solutions when specific features are not directly available in the target ETL tool.

For further reading, check out our Data Modernization eBook that takes a deeper look at migrating to cloud-native ETL/ELT.

Step 5: Testing, Monitoring and Cutover

Thorough testing is essential to ensure the success of your ETL modernization project. Implement robust monitoring and alerting to identify and address issues promptly. Develop a detailed cutover plan to minimize disruptions.

  • Unit and Integration Testing: Unit testing of converted ETL jobs is crucial. Using production-like data helps identify data-specific bugs effectively.
  • Functional Testing: The code must be tested with various data sets to ensure the job’s functionality.
  • Negative Testing: Negative testing should be performed to ensure the code behaves as expected with invalid data.
  • Performance and Cost-Based Testing: This testing should be performed to verify that the correct compute configuration is selected for optimized execution times and cost efficiency.
  • UAT:By carefully planning and executing UAT, you can ensure a smooth transition to the new ETL system, minimize disruptions, and enhance overall data management effectiveness.
  • Cutover:The cutover process involves finalizing migration activities and backups, scheduling downtime, synchronizing data, and switching to the new ETL system. It includes monitoring and validating system performance, providing user support, documenting the transition, and eventually decommissioning the legacy system while ensuring data retention.

Conclusion

So now we have covered the challenges of legacy ETL, talked about how cloud modernization can transform your data management, provided some customer examples, and outlined a step-by-step guide for ETL modernization.

By following this five-step process, you can successfully modernize your ETL process, improve data efficiency, and gain valuable insights to drive your business forward. Remember, the benefits of ETL modernization extend beyond technical improvements. By embracing this transformation, you’ll empower your organization to make data-driven decisions, enhance operational efficiency, and gain a competitive edge in the market.

If you are ready to take your explorations to the next level, visit our Automated ETL Migration solution page for a complete breakdown of a proven methodology for source ETL analysis, code conversion and testing/validation.

The post The Legacy ETL Dilemma – Part 2: A Step-by-Step Guide to Modernize Your ETL Process appeared first on Bitwise.

]]>
The Legacy ETL Dilemma – Part 1: Why Modernize Your ETL in the Cloud https://www.bitwiseglobal.com/en-us/blog/the-legacy-etl-dilemma-part-1-why-modernize-your-etl-in-the-cloud/ https://www.bitwiseglobal.com/en-us/blog/the-legacy-etl-dilemma-part-1-why-modernize-your-etl-in-the-cloud/#respond Fri, 04 Oct 2024 12:39:08 +0000 https://www.bitwiseglobal.com/en-us/?p=49294 Introduction Data is like the fuel that keeps modern businesses running. It’s important for making smart decisions and staying ahead of the competition. Traditionally, ETL (Extract, Transform, Load) processes have been the go-to for data integration. However, legacy ETL systems are increasingly creating new challenges for organizations. This blog, the first in a two-part series, ... Read more

The post The Legacy ETL Dilemma – Part 1: Why Modernize Your ETL in the Cloud appeared first on Bitwise.

]]>
Introduction

Data is like the fuel that keeps modern businesses running. It’s important for making smart decisions and staying ahead of the competition. Traditionally, ETL (Extract, Transform, Load) processes have been the go-to for data integration. However, legacy ETL systems are increasingly creating new challenges for organizations.

This blog, the first in a two-part series, will explore the challenges faced by legacy ETL systems in today’s data-driven world. We’ll discuss how these systems are struggling to keep up with the increasing volume, variety, and velocity of data. Additionally, you can learn more about the benefits of modernizing ETL processes using cloud-based solutions and AI/ML technologies. By the end, you’ll understand why ETL modernization is essential for businesses to remain competitive and drive innovation.

The Legacy ETL Landscape

Legacy ETL systems have been around for decades, serving as the backbone for data integration and processing. These systems were designed for structured data from relational databases and have limited capabilities to handle the diverse and voluminous data we encounter today. Some common challenges with legacy ETL systems include:

  • Distribution of data at different locations:Traditionally, separate data silos were established at various locations due to the limited scalability of existing data centers. For example, in the retail industry, different pricing systems may be created for distinct customer segments, such as loyal or regular customers. These systems would be housed in different locations, leading to multiple issues such as high maintenance costs and increased latency.
  • Scalability issues: In traditional systems, scalability issues arose when data volumes increased significantly. For instance, in the retail industry, product sales surge during the festive season, causing invoice data to quadruple compared to regular periods. Because traditional systems lacked scalability, businesses had to maintain infrastructure capable of handling this 4X data volume throughout the entire season, resulting in high maintenance costs.
  • High maintenance costs: In addition to the scalability issues leading to high maintenance costs, other factors include maintaining the physical security of data servers, creating backup systems for disaster recovery, retaining resources with specialized skill sets to manage cybersecurity and a lot more.
  • Limited flexibility:Traditional systems were designed for structured data, such as flat files and RDBMS. However, nowadays, various semi-structured and unstructured data sources are available, making it extremely difficult for traditional systems to manage.

Why Modernize ETL?

The digital transformation wave necessitates a shift from legacy ETL systems to more robust, scalable, and flexible cloud ETL solutions. It not only overcomes the challenges mentioned with legacy ETL process but also helps you with modern requirements such as the following:

  • Increase in real-time data processing use cases: Although legacy ETL tools can handle real-time data processing, they often encounter issues such as performance bottlenecks, latency problems, resource intensity, and integration challenges. These issues can be more effectively managed with modern cloud-based platforms while migrating ETL workloads to the cloud.
  • AI and machine learning integration: Integrating AI and machine learning with cloud platforms is simpler than with on-premises setups as they offer easy access to tools, frameworks, and collaborative features, making it more flexible and resource-efficient for developing and deploying AI models.

To illustrate, Bitwise recently worked with a transportation ministry in Canada that faced limitations with its legacy data integration platform and set a strategy to migrate Informatica ETL to Azure Data Factory (ADF) to leverage the advanced capabilities of the Azure Data & AI ecosystem.

The Need for ETL Modernization

The limitations of legacy ETL systems are hindering businesses. Cloud-based ETL solutions offer a more scalable, flexible, and cost-effective approach. By modernizing with a cloud-based ETL system, you can:

  • Improve data processing speed and efficiency
  • Enable real-time data analytics
  • Integrate AI and machine learning capabilities
  • Reduce operational costs
  • Enhance data security and compliance

A great example comes from a multi-national retail chain that had long-running ETL jobs in its legacy system in DataStage. With automated ETL migration of DataStage to Azure Data Factory, Bitwise helped the retailer optimize long-running jobs to enhance the efficiency of the data integration system.

Conclusion

Embracing modernization is not just an option but a necessity for businesses seeking to thrive in the digital age. In our blog post, 3 Real-World Customer Case Studies on Migrating ETL to Cloud, we explore successful ETL migrations covering different legacy systems and cloud platforms to highlight the shift in technologies driving today’s data integration needs.

Coming up next in Part 2 of this two-part series, we will delve into the specific steps involved in migrating legacy ETL systems to the cloud. We’ll cover topics such as choosing the right cloud platform, designing a migration strategy, and leveraging automation tools to streamline the process.

By making this strategic shift, organizations can improve operational efficiency, gain valuable insights, and ultimately achieve a competitive advantage. It’s time to break free from the legacy ETL constraints and embark on a journey towards a data-driven future.

The post The Legacy ETL Dilemma – Part 1: Why Modernize Your ETL in the Cloud appeared first on Bitwise.

]]>
https://www.bitwiseglobal.com/en-us/blog/the-legacy-etl-dilemma-part-1-why-modernize-your-etl-in-the-cloud/feed/ 0
Boost Your Application Security: How to Leverage GCP Cloud Armor for an Extra Layer of Protection https://www.bitwiseglobal.com/en-us/blog/boost-your-application-security-how-to-leverage-gcp-cloud-armor-for-an-extra-layer-of-protection/ https://www.bitwiseglobal.com/en-us/blog/boost-your-application-security-how-to-leverage-gcp-cloud-armor-for-an-extra-layer-of-protection/#respond Fri, 16 Aug 2024 12:13:18 +0000 https://www.bitwiseglobal.com/en-us/?p=48845 What is Cloud Armor? Cloud Armor is a global Web Application Firewall (WAF) and DDoS mitigation service provided by GCP. It can be positioned in front of your internet-facing applications to act as a security shield, filtering malicious traffic before it reaches your backend servers. Cloud Armor provides a multi-layered defense against various risks as ... Read more

The post Boost Your Application Security: How to Leverage GCP Cloud Armor for an Extra Layer of Protection appeared first on Bitwise.

]]>
What is Cloud Armor?

Cloud Armor is a global Web Application Firewall (WAF) and DDoS mitigation service provided by GCP. It can be positioned in front of your internet-facing applications to act as a security shield, filtering malicious traffic before it reaches your backend servers. Cloud Armor provides a multi-layered defense against various risks as given below.

DDoS Attacks: Cloud Armor assures availability of service during traffic surges and safeguards your applications from volumetric (L3/L4) and Layer 7 DDoS attacks. This is how you can use GCP Cloud Armor to protect against DDoS attacks.

Web Application Attacks (WAF): You can mitigate common web vulnerabilities like SQL injection and cross-site scripting (XSS) by pre-configured WAF rules based on OWASP Top 10 risks.

Cloud Armor Benefits

  • Enhanced Security: Cloud Armor safeguards your applications from a broad spectrum of threats and offers a comprehensive security solution.
  • Improved Performance: Cloud Armor reduces the load on your backend servers and enhances application performance by filtering malicious traffic at the edge.
  • Simplified Management: It provides a user-friendly interface for managing security policies and monitoring traffic patterns.
  • Global Scale: Consistent protection across all your GCP regions is assured by globally distributed network ensures.

Reference:

GCP Cloud Armor Architecture Diagram: Illustrates a web application protected by Cloud Armor, including authentication, load balancing, Compute Engine, GKE, and Cloud DNS.

  • Users access your application on the internet.
  • Traffic is routed through Cloud Load Balancing, which can be integrated with Cloud Armor.
  • Cloud Armor’s WAF engine inspects incoming traffic, filtering out malicious requests based on pre-configured rules or custom policies.
  • Legitimate traffic is forwarded to your application servers / backend services.

Sample Policy for reference –

Pros and Cons of using Cloud Armor

Benefits of using GCP Cloud Armor for web application security:

  • Provides web application vulnerabilities and security against DDoS attacks.
  • Better application performance and availability.
  • User-friendly interface and simplified security management.
  • Scalable protection that adapts to your application’s traffic patterns.

Drawbacks of using GCP Cloud Armor:

  • Additional cost associated with Cloud Armor usage.
  • Might need configuration adjustments for existing applications.
  • Might add slight latency because of additional processing at the edge.

Cost Considerations

The charges of configuring GCP Cloud Armor for optimal protection are based on incoming and outgoing request counts. You can leverage GCP’s free tier for limited usage. Pay-as-you-go pricing applies for exceeding the free tier limits. Refer to GCP’s pricing documentation for detailed cost information

https://cloud.google.com/armor/pricing.

Conclusion

GCP Cloud Armor offers a comprehensive security solution for your internet-facing applications on Google Cloud Platform. It safeguards your applications from a wide range of threats, improves performance, simplifies management, and provides global protection. While there are additional costs and potential configuration adjustments, the benefits of enhanced security, improved application health, and user-friendly management outweigh the drawbacks for most organizations. Contact Us to discuss your application security needs with our experts and determine if Cloud Armor aligns to your objectives.

The post Boost Your Application Security: How to Leverage GCP Cloud Armor for an Extra Layer of Protection appeared first on Bitwise.

]]>
https://www.bitwiseglobal.com/en-us/blog/boost-your-application-security-how-to-leverage-gcp-cloud-armor-for-an-extra-layer-of-protection/feed/ 0