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.

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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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Agile Testing Services

Functional Test Automation and faster feedback are the essential ingredients to create a successful Agile testing recipe.

Agile test automation capabilities can simplify testing of software products developed using Agile methodology.

Recommendations for successful implementation of Agile testing:

• Thorough understanding of Agile test quadrants.
• QA team is conversant with a wide array of testing tools and Agile methodologies (Scrum, Kanban, etc.).
• Application of inverted test pyramid in Agile.
• Integration and execution of functional/non-functional tests on CI servers (i.e. Jenkins).
• Incorporation of behavior-driven development (BDD) frameworks for acceptance testing.

Your organization will benefit from Agile testing implementation since culture shift will be easier when your QA team becomes conversant with Agile principles and hands-on work in Agile.

In addition, probable adoption of DevOps becomes easy in your evolution of deployment methodology.

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What successful meetings look like in SharePoint

• When setting up a meeting in Microsoft Teams, set the agenda.
• Set the recording to ‘on’ so that people who are not able to attend can watch it later. This also allows you to go back and catch something you missed.
• Capture meeting notes on the fly. Multiple attendees can co-author notes in Teams in real-time.
• Convert the meeting notes into action items and track them using Planner.
  • Assign owners to the action items
  • Assign due dates
  • Create sub-tasks
  • Track progress
• Use real-time Co-authoring features to work on presentations and documents.
• For recurring meetings, use the blocked/in-progress items from Planner as agenda items for the next meeting.

The post How to Communicate Better when Everyone Works Remote using Office 365 appeared first on Bitwise.

Challenges with the Legacy ETL Platforms

How did we get here? Over time, organizations have a build-up of legacy platforms, often through mergers and acquisitions, or bringing in different tools for specific use cases. Integration of other transformation assets, like SQL packages, adds to the mix.

Why is it a problem? With multiple tools come multiple licenses and exploding costs. The combination of legacy platforms and SQL packages results in complexity and presents challenges for governance and lineage. Also, legacy tools were not built for big data or cloud, which are now part of the ecosystem.

According to Dzone, The traditional ETL process is slow, time-consuming, and resource-intensive.

As traditional, on-premise enterprise data warehouses are moving to cloud data lake environments, organizations see a need for modern ETL platforms like Azure Data Factory and AWS Glue to meet their requirements.

Even if companies do not have multiple ETL tools, there is often a need to upgrade their old platform in order to take advantage of newer capabilities like self-service BI and real-time analytics, which can require migration effort. Businesses must evaluate their ETL procedures and consider switching tools as technology continues to advance. So, we will discuss the necessity of migrating from one ETL to another in 2023.

1. Enhanced features and functions:

The new “tool’s” enhanced capabilities and features are one of the primary reasons to switch from one ETL tool to another. Newer ETL tools with advanced features like cloud integration, real-time data processing, and AI/ML-powered data transformation have emerged as a result of technological advancements. Businesses may benefit from increased speed, accuracy, and efficiency as a result of these features.

2. Cost reduction:

Businesses may be able to save money by switching to a new ETL tool. Support and maintenance costs for older ETL tools may be significant, which can add up over time. Additionally, more recent ETL tools frequently provide more adaptable pricing options, such as subscription-based or pay-per-use pricing, which may prove to be more cost-effective in the long run.

3. Scalability:

Data processing requirements rise with business expansion. Businesses can better manage their expanding data requirements by migrating to a new ETL tool with scalability. Modern ETL services are intended to deal with large volumes of information and can scale processing resources up or down as needed.

4. Compliance and security enhancements:

In this digital age, data security and privacy are of the utmost importance. It’s possible that older ETL tools lack the necessary security features to safeguard sensitive data. Businesses can ensure that their data complies with GDPR, CCPA, and HIPAA regulations by migrating to a new cloud-native ETL tool.

5. Increased productivity:

With drag-and-drop interfaces and pre-built connectors, newer ETL tools are designed to be easier to use. Improved productivity and streamlined data integration procedures are two potential outcomes of this. Furthermore, newer ETL tools frequently offer better automation capabilities, decreasing the requirement for manual “intervention” and opening up assets for different undertakings.

The post 5 reasons to migrate one ETL to another ETL in 2025 appeared first on Bitwise.

What is Conversational UI

Conversational UI is the next-gen interface for voice-based mode of interactions that can be integrated with voice-enabled devices. Previously, if you needed anything from any database or server you interacted with a web application, mobile app, or chatbot.

Conversational UI provides an experience where you can interact with any device through voice. The interface uses a machine that converts voice to text (content) which can retrieve the appropriate data from the database. Now you can ask your query verbally – such as “what is my financial report for today?”

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The Cloud Data Lake Journey – Roadblocks and Successes

When planning an enterprise cloud strategy, choosing the right cloud platform is just the beginning. In this Premiere Presentation, Bitwise will explore the roadblocks and successes for building a Business Analytics system in the cloud, with lessons taken from a cloud data lake initiative at a leading worldwide provider of payment technology services.

The post Bringing Cloud into the Big Data and Analytics Discussion at TDWI Savannah appeared first on Bitwise.

An Automated Approach to Migrating PL/SQL to Informatica

How do we do this? First, we use our automated PL/SQL Script Analyzer to create a visualized Workflow Document that helps to easily understand the code and quickly generate a Mapping Document. Once the Mapping Document has been validated, we can plug the file into our automated ETL Conversion Engine, which re-engineers the PL/SQL to the target ETL. Upon completion, a Conversion Report shows that the process was completed successfully.

Sounds pretty cool? Hundreds of data professionals at Informatica World 2017 thought so, too. If you didn’t get a chance to stop at our booth, or if you were unable to attend the event, don’t worry – we have a recorded webinar on An Automated Approach to Migrating PL/SQL to Informatica that will walk you through the whole process in under 30 minutes.

Take comfort in knowing that standing by and letting your PL/SQL programs fade into obsolescence, or taking the labor intensive path of migrating manually are no longer your only options.

Looking for more information on ETL Conversion? Check out this LinkedIn article, Automated Migration of ETL Solutions, by Rick van der Lans, Analyst/Owner at R20/Consultancy for a brief analysis of the ETL migration challenge.

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