You might have heard migration various times but not much about ETL, when talking about it you might think what it is and what it is used for and what are benefits. In this blog of ETL tool we will discuss about various cloud platforms that have ETL tools and how migration can be done.
In this blog we will cover:
- What is an ETL tool?
- Common Use Cases of ETL Tool
- ETL Tools offered by cloud providers (AWS, Azure, Google, Oracle & IBM)
- Migration Overview & Path
- SQL migration from on-premises to Azure SQL database
- Conclusion
- FAQ’s
What is an ETL tool?
An ETL tool is a software to Extract, Transform, and Load that automates the process of extracting data from various sources, transforming it into a consistent and usable format, and then loading it into a target system, such as a data warehouse or data lake.
Now let’s understand ETL individually:
- Extract: The ETL tool retrieves data from different sources, which could be databases, flat files, applications, or even social media feeds.
- Transform: The extracted data is often messy and inconsistent. The ETL tool cleanses the data, filters out errors, and converts it into a format that’s compatible with the target system. This may involve standardizing units, combining data from multiple sources, or calculating new fields.
- Load: Finally, the transformed data is loaded into the destination system, where it can be used for analytics, reporting, backup and restore or machine learning.
Common Use Cases of ETL Tool
There are various uses defined below of ETL tools used in day to day tasks:
- Data Warehousing: Building and maintaining data warehouses is a primary use case for ETL tools, where data from various sources is consolidated into a central repository for analysis and reporting.
- Data Migration: Migrating data from on-premise databases to the cloud, or between different cloud platforms, is facilitated by ETL tools, helping to ensure that data remains intact and secure during the transfer.
- Business Intelligence and Analytics: ETL tools prepare and curate data specifically for analysis, making it suitable for business intelligence tools that help derive insights and make data-driven decisions.
ETL Tools offered by cloud providers (AWS, Azure, Google, Oracle & IBM)
Each major cloud provider offers its own set of ETL tools designed to facilitate data integration (Extract, transformation, and loading) processes tailored to its environment. Here’s an overview of various ETL tools from Azure Cloud, Google Cloud Platform (GCP), AWS, IBM Cloud, and Oracle Cloud:
Azure Cloud (Microsoft)
| Feature | On-Premise (SQL Server Integration Services) | Cloud (Azure Data Factory) |
| Deployment | Installed and runs on local servers | Hosted in Azure, Microsoft’s cloud platform |
| Location | Within the organization’s data center | No physical infrastructure concerns for the user |
| Infrastructure Management | Managed by the organization’s IT team; requires setup and maintenance of physical servers | Managed by Microsoft |
| Scalability | Scalability is limited by local hardware capabilities | Highly scalable; resources can be adjusted dynamically based on workload |
| Initial Cost | High initial investment in infrastructure and licensing fees | Pay-as-you-go pricing model reduces upfront costs |
| Maintenance | Handled by the organization’s IT department, including updates and upgrades | Maintenance, updates, and scalability managed by Microsoft |
| Data Security | Full control over security measures, complies with the organization’s internal policies | Microsoft ensures security, compliance with industry standards and regulations |
Azure Data Factory: This is a fully managed, serverless data integration service that allows users to create, schedule, and orchestrate ETL/ELT workflows. It supports connecting to a wide variety of data stores, provides a rich set of transformation capabilities, and integrates seamlessly with other Azure services like Azure Synapse Analytics, Blob Storage, and SQL Database.
Amazon Web Services (AWS)
AWS Glue: A serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development. AWS Glue provides both visual and code-based interfaces to make data integration easier.
Google Cloud Platform (GCP)
Google Cloud Dataflow: A fully managed service for stream and batch data processing that can be used for ETL tasks. It’s built on Apache Beam, which provides a model for defining both batch and streaming data-parallel processing pipelines.
Cloud Data Fusion: This managed service provides a visual interface for building ETL pipelines. It’s user-friendly and integrates well with other GCP services, making it a good choice for less technical users.
Dataproc: This service allows you to run Apache Spark and other open-source big data frameworks on Google Cloud. It offers a lot of flexibility for building custom ETL pipelines but requires more technical expertise.
| ETL Tool | Best Use Cases | Ideal For |
| Google Cloud Dataflow | Real-time data processing, Complex ETL tasks, Both stream and batch data processing | Situations requiring serverless operation and minimal operational overhead, Scenarios needing high scalability and low-latency processing |
| Google Cloud Data Fusion | Data integration tasks requiring a visual interface, Connecting disparate data sources without extensive coding | Users preferring a graphical interface, Rapid development and deployment of data pipelines |
| Google Cloud Dataproc | Large-scale data processing using Hadoop/Spark, Custom job tuning and configurations for big data tasks | Existing Hadoop and Spark users, Projects that benefit from open-source flexibility and extensive big data tools |
Oracle Cloud
Oracle Data Integrator (ODI): This tool offers a unique ELT (Extract, Load, Transform) architecture that helps improve performance and reduce data integration costs even across diverse systems. Oracle also provides the Oracle Integration Cloud, which offers capabilities for automating processes, integrating applications, and driving real-time analytics
| Feature | On-Premise (Oracle Data Integrator, ODI) | Cloud (Oracle Integration Cloud, OIC & ODICS) |
| Deployment | Installed on the organization’s local servers | Hosted on Oracle Cloud Infrastructure (OCI) |
| Location | Managed by the organization’s IT team; requires setup and ongoing maintenance of hardware | Managed by Oracle; no physical infrastructure concerns for users |
| Infrastructure Management | Requires setup and ongoing maintenance of hardware by the organization’s IT team | Managed by Oracle |
| Scalability | Scalability is limited by local hardware capabilities | Easily scalable with cloud resources, can adjust dynamically based on demand |
| Initial Cost | Requires initial investment in hardware and software licenses | Generally lower initial costs with subscription-based pricing models |
| Maintenance | Organization is responsible for updates, upgrades, and general maintenance | Oracle handles maintenance, including regular updates and patches |
IBM Cloud
IBM Datastage: IBM DataStage is a powerful ETL (Extract, Transform, Load) tool designed for data integration tasks within businesses. It offers a graphical interface for building data pipelines that can extract data from various sources, transform it to a desired format, and then load it into target databases, data warehouses, or other applications. DataStage is known for its ability to handle complex transformations, manage large data volumes, and seamlessly integrate with enterprise applications.
| Feature | On-Premise (IBM DataStage) | Cloud (IBM Cloud Pak for Data) |
| Deployment Location | Locally on the organization’s own servers and hardware | Hosted on IBM Cloud or other cloud environments |
| Infrastructure Management | Managed by the organization’s IT team | Managed by IBM or the cloud provider |
| Scalability | Limited by in-house server capacity | Easily scalable to meet demand without physical constraints |
| Initial Cost | Higher due to necessary infrastructure and setup costs | Lower initial cost, with payment scaling with usage |
| Maintenance | Responsibility of the organization’s IT department | Handled by IBM, including updates and patches |
| Data Security | Complete control over security measures | Security managed by IBM, with compliance to cloud security standards |
Migration Overview & Path
Migrating from IBM DataStage to Azure Data Factory (ADF) is a complex process involves several stages, including planning, preparation, execution, and validation. This transition not only changes the technical environment but also potentially alters data management workflows. Here’s a structured overview and path for such a migration, including key considerations:
1. Assessment and Planning:
- Evaluate Current Environment: Understand the existing DataStage jobs, workflows, and dependencies. This includes assessing data sources, transformations, destinations, and any custom code.
- Define Migration Objectives: Establish what needs to be achieved with the migration to Azure Data Factory, such as increased scalability, reduced costs, or enhanced analytics capabilities.
- Tool Compatibility Check: Determine which DataStage functionalities are directly compatible with ADF and identify any components that require redesigning or alternative solutions
2. Proof of Concept (PoC)
- Select a Subset of Workflows: Pick representative DataStage jobs to recreate in ADF. This subset should include various types of transformations and data loads.
- Develop in ADF: Implement the chosen workflows in ADF using pipelines, data flows, and linked services.
- Evaluate and Adjust: Assess the performance and functionality against the original DataStage jobs. Make necessary adjustments to configurations and design.
3. Migration Execution
- Data Integration Runtime Setup: Configure the Azure Integration Runtime within ADF to manage data movements and transformation jobs.
- Incremental Migration: Convert and migrate DataStage jobs to ADF incrementally. Group jobs by complexity, priority, or functional similarity.
- Automation and Scheduling: Implement scheduling within ADF or integrate with existing enterprise scheduling tools if needed.
- Monitoring and Logging: Set up monitoring using Azure Monitor and other logging tools to track the performance and success of the workflows.
4. Testing and Validation
- Parallel Runs: Run the new ADF processes in parallel with the existing DataStage jobs to compare outputs and performance.
- Data Validation: Check data integrity and accuracy across sources and destinations to ensure that all data is processed correctly.
- Performance Tuning: Optimize ADF pipelines for performance, potentially utilizing Azure’s other services like Azure SQL Database, Blob Storage, and Data Lake for enhanced processing speeds.
5. Cut-Over and Go-Live
- Final Migration: Once testing is complete and results are satisfactory, finalize the migration of all jobs to ADF.
- Go-Live: Switch processing from IBM DataStage to Azure Data Factory officially.
- Post-Go-Live Support: Provide support to address any immediate issues and conduct additional optimizations as necessary.
SQL migration from on premise to Azure SQL database:
Also if you want to do a simple database migration from sql server on premise to Azure SQL then you have different tools to utilise, I am sharing you the content of that too.
To give a brief overview on Migration, I have share a workflow of Migration. You can refer to official Documentation for more information : SQL Server to Azure SQL Database: Migration overview – Azure SQL Database | Microsoft Learn
Prerequisite:
For your SQL Server migration to Azure SQL Database, make sure you have:
- Install Azure Data studio inside your VM which contains database.
- Install the Azure SQL migration extension for Azure Data Studio inside the VM from which migration has to be done
- Create a target Azure SQL Database. inside your subscription in which data has to be migrated.
- Configure the connectivity and proper permissions to access both the source and target.
- Review the database engine features available in Azure SQL Database.
- Register Microsoft.datamigration service provider inside your subscription
Assessment:
In this step, we will do assessment of the database environment inside VM.
To assess your environment using the Azure SQL Migration extension, follow these steps:
- Open the Azure SQL Migration extension for Azure Data Studio.
- Connect to your source SQL Server instance and Azure SQL data base inside Azure Data studio.
- Click the Migrate to Azure SQL button (to find this, right-click on your local database on left pane and you will find option Azure SQL Migration) in the Azure SQL Migration wizard in Azure Data Studio
- Select databases for assessment, then click on next
- Select your Azure SQL target, in this case, Azure SQL Database (Preview)
- Click on View/Select to review the assessment report
- Look for migration blocking and feature parity issues. The assessment report can also be exported to a file that can be shared with other teams or personnel in your organization.
- Determine the database compatibility level that minimizes post-migration efforts.
Migration:
To perform Migration follow the steps below:
- Download and install Azure Data Studio and the Azure SQL migration extension.
- Launch the Migrate to Azure SQL Migration wizard in the extension in Azure Data Studio.
- Select databases for assessment and view migration readiness or issues (if any). Additionally, collect performance data and get the right-sized Azure recommendations.
- Select your Azure account and your target Azure SQL database from your subscription.
- Select the list of tables to migrate.
- Create a new Azure Database Migration Service using the wizard in Azure Data Studio. If you’ve previously created an Azure Database Migration Service using Azure Data Studio, you can reuse the same if desired.
- Optional: If your backups are on an on-premises network share, download and install self-hosted integration runtime on a machine that can connect to the source SQL Server, and the location containing the backup files.
- Start the database migration and monitor the progress in Azure Data Studio. You can also monitor the progress under the Azure Database Migration Service resource in Azure portal.
Conclusion
In conclusion, we have explored a valuable resource that offers a comprehensive overview of ETL tools, particularly for those interested in understanding ETL tools, migrating data to the cloud and migrating from IBM DataStage to Azure Data Factory. Furthermore, we have also discussed various ETL tools in different cloud environments, providing a detailed analysis of their capabilities and applications. Also discussing various ETL tools in different cloud environments. Remember that Planning, testing, and monitoring are crucial for a successful and streamlined data migration process.
FAQs
Q1. What is ETL and SQL?
Ans: ETL is a broader process for moving and preparing data for analysis, while SQL is a language for interacting with relational databases. ETL tools can leverage SQL for data extraction and transformation, but SQL itself has limitations for large-scale data movement and complex transformations. They are complementary tools that can work together for effective data management.
Q2. What is ETL tool in software?
Ans: ETL tools are essential software components for data-driven businesses. They streamline the data integration process, improve data quality, and ultimately enable better decision-making based on accurate information.
Q3. Which is the popular ETL tool?
Ans: There are different ETL tools available from different various Providers: Microsoft Azure Data Factory (ADF), Amazon Web Services (AWS) Glue, Google Cloud Dataflow, IBM DataStage, Oracle Data Integrator (ODI)
Q4. Is Excel an ETL tool?
Ans: While Excel can handle basic ETL tasks, it is not a full-fledged ETL tool. Dedicated ETL tools like ADF, AWS Glue, Airbyte, Fivetran, Stitch Data etc. are better suited for complex enterprise-level ETL requirements. These tools provide more robust, scalable and maintainable ETL pipelines compared to using Excel.
Related/References
- Microsoft Azure Data on Cloud Bootcamp: Hands-On Labs & Projects for Job-Ready Skills
- Azure Data Lake For Beginners: All You Need To Know
- Azure SQL Database: All you need to know about Azure SQL Services
- Azure Cosmos DB: All you need to know about Azure Cosmos DB
- Structured Data Vs Unstructured Data Vs Semi-Structured Data
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