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.

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.

Smart Alert Mechanism for Proactive Monitoring

Common production support challenges include alerting the production support team for failures of critical applications, providing a direct link to open possible steps to resolve the failed jobs, immediate attention for critical job streams, and broadcasting the possible delay in the SLA to the end user.

Using a chat bot tool, such as Google Chatbot, can solve challenges for alerting and providing relevant help to production engineers and notifying all actional and critical alerts. A customizable solution should include DAG logs, SOPs and failure history coupled with comprehensive documentation. Solutions should be highly integrated to add different sources of alerts and integrate with tools like Service Now for incident lifecycle management (logging to closure).

Benefits include early problem detection and improved user experience, effective resource utilization due to reduced efforts, smart notifications with actionable insights, reduced turnaround time for the issue resolution that can work within the existing platform and leverage the available technology stack. 

Check out a recent case study on smart alert notification via chat bot to learn how Bitwise built a real-time notification system with chat functionality for a global payment provider and reduced resolution time for critical issues. 

Conclusion

There is little doubt that success in today’s markets depends on an organization’s ability to leverage data and insights to act quickly in a competitive landscape. Utilizing the latest technologies and machine learning practices to optimize proactive monitoring for your data platform production support is essential to get the most out of your data lake and data warehouse environments.

By leveraging accelerators like the AI/ML model for predicting job completion and smart alert notification system, production support teams can stay ahead of potential problems that could impact critical applications.

Bitwise has extensive experience in Data Warehouse and Business Intelligence and associated best practices, which uniquely positions the consulting and services company to provide effective and stable Data Platform Support solutions ensuring a seamless experience to business users through high availability of data for consumption.

The post Proactive Monitoring of Data Platform using Machine Learning appeared first on Bitwise.

Benefits of Cloud-Native Architecture

The transformation of traditional on-premise data platform to cloud-native architecture offers several benefits to organizations:

• Faster Time-to-Market –DevOps practices and continuous delivery enabling organizations to accelerate their time-to-market.
• Flexibility and Portability – enables the ability to run applications on various cloud platforms or even on-premise infrastructure without significant modifications.
• Cost Efficiency – resource optimization and flexible, scalable pricing models can help with efficient utilization of resources.

Cloud-Native ETL Paradigm

The cloud-native paradigm has revolutionized the way organizations approach data integration and ETL processes by offering the ability to scale, automate and optimize their data integration processes in a highly flexible yet cost effective manager. 

Another factor of the cloud-native paradigm is shifting from the traditional approach of ETL (extract, transform, load) to ELT (extract, load, transform) which provides a faster and more scalable option for processing big data workloads.

ETL Tools in the Cloud-Native Environment

Cloud-native ETL tools offer unique features, integrations, and pricing models, so it’s important to evaluate based on your specific requirements. Some of the leading ETL services designed for the cloud include:

• AWS Glue – fully managed ETL service that offers a serverless approach to ETL that automatically handles infrastructure provisioning and scaling.
• Azure Data Factory (ADF) – cloud-based data integration service that enables users to create and schedule data-driven workflow.
• Intelligent Data Management Cloud (IDMC) – Informatica’s unified platform for companies to integrate, manage and govern their information.

ETL Migration and Modernization

Organizations can take advantage of the latest AI capabilities with maturity of cloud-native ETL/ELT services, providing a tremendous opportunity to modernize legacy data platforms in the cloud. Depending on your business objectives, GUI-based services like AWS Glue Studio, ADF and IDMC are viable options for building modern data pipelines. Many organizations are also modernizing ETL with PySpark code for execution through services like Azure Databricks, Microsoft Fabric, AWS Glue or EMR.

Challenges of Migrating Traditional ETL to Cloud-Native Approach

Migrating traditional ETL processes to a cloud-native approach presents major challenges that organizations need to address, including:

• Data Volume and Velocity
• Data Integration Complexity
• Data Security and Compliance
• Skill Set and Expertise
• Data Latency and Performance
• Cost Management

Achieving Data Modernization Success

With over 12 years of ETL migration experience in converting over 30,000 ETL applications from one tool to another, Bitwise deeply understands the challenges and solutions for achieving a successful data modernization initiative. Based on our experience we recommend an automated ETL migration approach to avoid the most common challenges that will set your modernization back – both in terms of time and cost.

Automated ETL Migration Methodology

There are many factors that contribute to a successful ETL migration from legacy system to cloud-native architecture. Using automation to accelerate each phase of migration and overcome common errors that typically come up when manually converting data workflows.

1. Migration Assessment – to initiate a cloud-native architecture transformation, it is essential to assess the current state of the technology landscape. Automated assessment tools can provide detailed analysis of the inventory and dependencies needed to architect the optimal solution in the target cloud system.
2. Code Conversion – using an automated code conversion solution  with a robust library of mappings is essential to avoiding unnecessary errors and workarounds when converting code from one ETL to another and preventing extra re-work efforts.
3. ETL Testing and Validation – whether you go with a GUI-based ETL service or PySpark executed through a programmatical data processing framework, the most important step (and often the most overlooked) is testing and validation. Using an automation-based validation approach can detect unseen problems before the code gets pushed to downstream analytics applications.

Getting Started

A recent survey from MIT Technology Review found that just over 50% of surveyed senior data and technology executives have undertaken or are implementing a data modernization project, and an additional 25% have plans to within two years.

With the leading cloud providers offering comprehensive, all-in-one data platforms – including Microsoft, AWS, Databricks, Snowflake and Informatica, the need for data modernization is critical to take advantage of the analytical and AI capabilities that these platforms promise and stay ahead of the competition.

To learn more about modernizing data to tap into the power of cloud-native Data & AI platforms, get your free copy of our Data Modernization eBook today to get started.

The post Data Modernization: eBook Overview for Transforming ETL in the Cloud  appeared first on Bitwise.

Source: www.informatica.com

Benefits of IDMC

Since IDMC is cloud-native at scale and AI-native at scale, it provides advantages over traditional data integration tools, especially for businesses modernizing their data warehouse, data lake, or analytics in the cloud. Benefits of IDMC include:

• Scalability and elasticity – handle massive data volumes with ease, scaling seamlessly up or down based on your needs.
• Cloud-native architecture – provides agility and cost-efficiency of the cloud for your data integration processes.
• Modern capabilities – offers advanced features like data quality management, data governance, and self-service data preparation.
• Reduced complexity – consolidate multiple tools and processes into a single platform, simplifying your data landscape.
• Faster time to insights – streamline data integration and access insights.

Considerations for Migrating Legacy ETL to IDMC

When it comes to migrating ETL to IDMC, applications can be grouped into PowerCenter and non-PowerCenter ETL. For PowerCenter applications, Informatica offers a Migration Factory tool that certified Platinum Partners (such as Bitwise) can use to accelerate migration of PowerCenter to IDMC. 

For other ETL applications, there are not many options other than using brute force to manually convert the jobs to IDMC. This is where Bitwise can de-risk and accelerate the project with our comprehensive tools and capabilities to migrate ETL tools like DataStage, Ab Initio and SSIS to IDMC. 

For either scenario, common migration challenges include identifying and mapping data sources and targets, handling data transformations and cleansing, ensuring data quality and consistency, and managing user access and security.  

See our Bitwise and Informatica page for more details on our ETL modernization partnership.

Planning your Migration to IDMC

There are many planned and unplanned challenges that can come up when migrating legacy ETL to IDMC. Here are some key steps for careful planning and execution to mitigate challenges and risks:

1. Assessment: Analyze your existing ETL landscape, identifying pain points and opportunities for improvement.
2. Target state definition: Define your desired end-state with IDMC, considering your data integration needs and future goals.
3. Migration strategy: Choose the appropriate migration approach, considering factors like complexity, risk tolerance, and time constraints.
4. Tooling and resources: Identify the necessary tools and resources for code conversion, data migration, and testing.
5. Phased approach: Plan a phased migration to minimize disruption and ensure business continuity.
6. Testing and validation: Rigorously test migrated pipelines and data to ensure accuracy and completeness.
7. Monitoring and support: Implement comprehensive monitoring and support processes to address any issues after migrating to IDMC.

Working with a specialized migration partner that utilizes the optimal combination of automation tools and frameworks can significantly reduce the time and cost of translating your ETL jobs to IDMC.

How Bitwise Accelerates ETL Migration to IDMC

To make ETL modernizations more efficient and less risky, Bitwise provides a comprehensive solution that encompasses automation tools, frameworks and expertise aimed at expediting the migration of legacy ETLs to cloud platforms like IDMC.

We take a phased approach to migration where we use automation tools at every stage of Assessment, Conversion, and Testing & Validation.

ETL Migration Automation Tools and Frameworks

• Source ETL Code Analyzer – conducting a thorough analysis of the source ETL code base is crucial for successful project planning and delivery. The Source ETL Code Analyzer tool provides valuable insights on complexities, uncertainties, intricacies, and data lineage that may impact the project. During the Assessment phase of migration, Bitwise uses the reports generated by the tool to make informed decisions on design, solution approach, and planning for subsequent operations.

• ETL Conversion Framework – utilizing our expertise combined with utilities such as Bitwise’s Automated ETL Converter tool and Informatica’s automated IDMC converter tools, we’ve streamlined the process of migrating from traditional ETL platforms like PowerCenter, DataStage, SSIS and even Ab Initio to cloud-native platforms like IDMC. Our framework, backed by extensive knowledge of mappings and workarounds, handles the complex tasks while our ETL Migration Specialists, who are well-trained and have access to the latest AI tools and vendor product teams, deliver the optimal solution.

• ETL Validation Framework – with its schema and data validation capabilities, this Python-based automated validation tool streamlines the ETL migration process from source to target and can be used for testing stored procedures, views and MV migrations. To successfully migrate legacy ETL to IDMC, Bitwise uses this automation tool for high-performance testing of relational, flat files, and cloud databases with support for both full and sample data validation as well as schema validation. Plus, the tool is lightweight, platform agnostic and seamlessly integrates with any CI/CD tools and schedulers to handle complex deployments.

To further accelerate delivery, we adhere to the Agile methodology throughout the different phases of modernization, incorporating predefined processes such as daily scrums, sprint reviews, retrospectives, and feedback loops with the client to ensure complete satisfaction.

Getting Started

Migrating legacy ETL to IDMC can be a transformative initiative for your organization, especially when modernizing your data warehouse or data lake environment to get your data ready for AI use cases. ETL code locked in legacy systems like Ab Initio, DataStage, SSIS or PowerCenter can hold a lot of complexity and lack of understanding due to poor documentation. Automation is the key to effectively unlock the potential of your data by modernizing ETL in a highly scalable cloud-native platform. 

To help organizations exploring ETL modernization, Bitwise offers a high-level assessment conducted through a questionnaire and sample jobs, enabling us to gather key information about the legacy ETL environment and target cloud platform. Explore our Automated ETL Migration solution to learn more and get started with a time and cost estimation for migration to help with your evaluations.

The post Migrating Legacy ETL to IDMC: What you need to know   appeared first on Bitwise.

PySpark programmatic ETL and GUI-based ETL are two different approaches to ETL (Extract, Transform, and Load).PySpark programmatic ETL involves writing ETL code in PySpark, a popular open-source distributed computing framework. This approach offers a number of advantages over GUI-based ETL tools, including:

• Flexibility: Programmatic ETL allows you to create custom ETL pipelines that are tailored to your specific needs. GUI-based ETL tools typically offer a limited set of pre-built components, which can be restrictive.
• Scalability: PySpark is a distributed computing framework, which means that it can scale to handle large datasets. GUI-based ETL tools are typically not as scalable.
• Automation: PySpark code can be easily automated using tools such as Apache Airflow or Prefect. This can free up your team to focus on more strategic tasks.
• Performance: PySpark is optimized for distributed computing, and it can take advantage of multiple cores and processors. This can lead to significant performance improvements over GUI-based ETL tools.

GUI-based ETL tools provide a graphical user interface for building and deploying ETL pipelines. This approach can be easier to get started with than programmatic ETL, but it can be less flexible and scalable.

Challenges of converting existing ETL code to PySpark code (potential targets include Azure Databricks, Synapse Notebooks, Fabric Notebooks, Spark Job Definition, EMR and AWS Glue)

Converting existing ETL code to PySpark code can be a challenging task. There are a number of reasons for this, including:

• Different programming paradigms: PySpark and traditional ETL tools use different programming paradigms. This means that the way code is written and executed differs significantly between PySpark and traditional ETL tools.
• Complexity of PySpark: PySpark is a complex framework with a wide range of features, especially if you are not already familiar with distributed computing paradigm.
• Lack of documentation: There is a lack of documentation on how to convert existing ETL code to PySpark code. This can make the conversion process challenging, especially if you are trying to convert complex ETL logic.
• Availability of skilled (PySpark) resources or learning curve : The challenge lies in finding proficient resources to handle the conversion of current ETL code to PySpark code. Since PySpark is a programming language and platform, there is a learning curve involved. It is imperative to allocate time and resources for training the team on PySpark and the new platform. The existing ETL developers may find it challenging to acquire proficiency in utilizing PySpark efficiently for ETL processes.
• There are various frameworks available in the market, such as Databricks, Synapse Notebooks, Fabric Notebooks, and AWS Glue, that offer built-in capabilities for spark programmatic ETL development. These frameworks optimize the underlying process execution and enable access to native cloud products, such as storage, key vault, and database. However, due to the abundance of available frameworks, it can be challenging to convert to a specific one.

Using automation to overcome challenges (Bitwise approach)

There are several things that you can do to overcome the challenges of converting existing ETL code to PySpark code for Azure Databricks and AWS Glue:

• Comprehensive Assessment: Conducts a comprehensive analysis of the source ETL code base, identifying any uncertainties, intricacies, and data lineage for better planning. (Any source code ETL analyzer tool will be helpful)
• Start small: Don’t try to convert all of your ETL code to PySpark at once. Start by converting a small subset of your code, and then gradually convert the rest of your code over time.
• Use a modular approach: Break down your ETL code into small, modular components. This will make the conversion process easier and more efficient.
• Use a code conversion tool: There are a number of tools that can help you to convert your existing ETL code to PySpark code. These tools can save you a significant amount of time and effort.
• Test your code thoroughly : Once you have converted your ETL code to PySpark, be sure to test it thoroughly to make sure that it is working correctly. (Any automation testing tool will be helpful)
• DevOps Cycle : DevOps can automate ETL pipeline build, testing, and deployment through CI/CD. Monitoring and alerting can detect issues and ensure smooth pipeline performance. Shift-left testing can detect and fix issues early in the development cycle. DevOps can improve collaboration between data engineering and other teams for timely and efficient ETL pipeline development and deployment.
• Deploy the new ETL solution: Once the testing is complete, deploy the new ETL solution in the Dev/QA/Production environment.
• Train the users: Train the users on the new ETL solution and provide them with the necessary documentation and support.
• Monitor and optimize the new ETL solution: Monitor the new ETL solution for any issues and optimize it for better performance (if required).

The post ETL Modernization with PySpark appeared first on Bitwise.

Traditionally, organizations had to choose between data warehouses and data lakes, each with its own strengths and limitations. Data warehouses excelled at providing structured and optimized data storage, but often struggled to accommodate the diversity and volume of modern data. On the other hand, data lakes allowed for flexible and scalable storage of raw data, but faced challenges when it came to organizing and querying that data effectively.

The Data Lakehouse, a term popularized by Databricks, aims to bridge this gap by combining the strengths of both data warehouses and data lakes. It offers a unified platform that supports structured and semi-structured data, enabling users to perform complex analytics, machine learning, and AI (Artificial Intelligence) workloads on a single architecture. The Data Lakehouse architecture provides the foundation for a more streamlined and efficient data management process.

The Databricks Advantage

Databricks, a leading unified analytics platform, has emerged as a pivotal player in the realm of Data Lakehouse solutions. The company’s cloud-based platform integrates data engineering, data science, and business analytics, providing organizations with a collaborative environment to drive innovation and insights from their data.

Key Features of the Databricks Data Lakehouse

Unified Analytics: Databricks’ platform offers a unified approach to analytics, enabling data engineers, data scientists, and analysts to work collaboratively on the same dataset. This eliminates data silos and promotes cross-functional insights.

Scalability: With the ability to process large volumes of data in parallel, Databricks Data Lakehouse solution scales effortlessly to accommodate growing data needs, ensuring high performance even as data volumes increase.

Advanced Analytics: The platform supports advanced analytics capabilities, including machine learning and AI, empowering organizations to derive predictive and prescriptive insights from their data.

Data Governance and Security: Databricks places a strong emphasis on data governance and security, providing features that ensure data quality, lineage, and access control, making it a reliable choice for enterprises dealing with sensitive data.

Ecosystem Integration: Databricks seamlessly integrates with a wide array of data sources, storage systems, and analytics tools, allowing organizations to build and deploy end-to-end data pipelines.

With Microsoft announcing general availability of Fabric in late 2023, organizations are lining up to take advantage of the latest analytical potential of the combined platform. For organizations with Teradata EDW, there can be a high degree of risk to completely modernize with Fabric. Bitwise helps organizations that want to quickly take advantage of cloud cost savings but are not ready for a complete modernization by migrating and stabilizing Teradata EDW to Teradata Vantage on Azure as a stopover solution before modernizing with a ‘better together’ Lakehouse/Fabric architecture for an advanced analytics solution in the cloud.

Organizations with legacy ETL (extract, transform, load) tools like Informatica, DataStage, SSIS (SQL Server Integration Services), and Ab Initio that want to take advantage of programmatical data processing frameworks like Azure Databricks utilizing data lakehouse architecture will find that migration can be a risky proposition due to incompatibility and high probability for human error. This is where Bitwise can overcome challenges and eliminate risk with its AI-powered automation tools backed by years of ETL migration experience to convert legacy code to PySpark for execution in Azure Databricks for improved flexibility to meet modern analytics and AI requirements.

The post Navigating the Data Modernization landscape and diving into the Data Lakehouse concept and frameworks appeared first on Bitwise.

ETL Migration Case Studies

1. Accelerated SSIS ETL Migration to Azure Data Factory

In this case study, Bitwise demonstrates how they assisted a client in migrating their existing SSIS ETL workflows to Azure Data Factory (ADF). The challenge was to ensure a seamless transition while optimizing performance and ensuring data integrity. Bitwise leveraged their expertise in both SSIS and ADF to streamline the ETL migration process. By rearchitecting and redesigning ETL workflows to fit the cloud-native ADF environment, they achieved increased scalability, flexibility, and reduced maintenance efforts. The success of the migration resulted in improved ETL performance and the client’s ability to harness the power of the cloud for data processing.

2. Migrate Legacy Informatica ETL Code to AWS Glue

This case study highlights Bitwise’s proficiency in migrating legacy Informatica ETL code to AWS Glue, a fully managed ETL service on Amazon Web Services. The client aimed to modernize their data processing by adopting cloud-based technologies. Bitwise tackled the migration by analyzing the existing Informatica workflows and transforming them into AWS Glue jobs. This involved optimizing the ETL logic to align with Glue’s serverless architecture, which offers benefits such as automatic scaling and cost efficiency. The successful ETL migration enabled the client to continue their data processing seamlessly in the cloud while taking advantage of AWS Glue’s capabilities.

3. Automated ETL Migration from DataStage to Azure Data Factory

In this case study, Bitwise showcases their expertise in migrating IBM InfoSphere DataStage ETL workflows to Azure Data Factory. The client’s goal was to transition from an on-premises DataStage environment to the cloud for enhanced agility and scalability. Bitwise facilitated the migration by thoroughly understanding the existing DataStage workflows and transforming them to fit the cloud-based ADF architecture. By utilizing its proprietary automation tools, Bitwise ensured a smooth transition without compromising data quality or performance. The outcome was a successful ETL migration that allowed the client to harness the benefits of cloud-based data processing with a solution architecture that minimizes Azure costs.

The three real-world customer case studies presented here exemplify the successful migration of legacy ETL workflows to cloud-based solutions. These migrations not only address the challenges posed by aging ETL systems but also tap into the immense potential of the cloud to augment scalability, flexibility, and performance. Check out our automated ETL migration page for a complete solution overview.

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