Without data-readiness, AI projects are at risk. To address this, modern data integration solutions like IBM® DataStage®, a key component of IBM Data Fabric, improve data usage for AI by empowering developers, engineers, and the enterprise with the following technology.
There are many aspects of next-generation DataSage that enhance the developer experience. To start, the design canvas offers a machine learning-assisted, user-friendly interface for designing and managing data pipelines. This no/low code UI with drag-and-drop functionality and high code extensibility allows users to visualize data flows while integrating and connecting to data from hundreds of supported data sources and targets from various files and data formats such as Parquet, JSON, Iceberg, XML, and many more. As a result, developers do not need to manually build pipelines and can instead transform data with reusable pipelines, greatly accelerating inception to production.
- Modern IBM DataStage also offers a suite of DataOps tools to enhance developer productivity. These capabilities include Git integration, which enables developers to easily sync and connect assets/projects, signed Git commands, send commands from the UI and the command line interface (CLI), unlock version control, and manage branches. Additionally, the DataStage operator framework allows repeatability of installation and upgrades along with unit testing and code quality checks to DataStage. With stages such as QualityStage, developers can automatically run data quality checks to deliver trustworthy, governed data. These DataOps tools in a modernized DataStage provide a simplified and enhanced development experience, empowering data integration teams to innovate at speed and scale.
Proficient data delivery is crucial for organizations to be data driven. If data is not delivered on time, enterprises will struggle with delayed insights, operational inefficiencies, and even compromised revenue. Due to these implications, data engineers need a robust data integration solution to ensure minimal latency and optimal performance to meet data delivery SLA’s.
There are two components that play a key role in a robust data integration solution: a performant engine and proactive data observability. With an engine to power data integration pipeline execution combined with proactive data observability to detect pipeline incidents earlier and resolve pipeline issues faster, data integration can effectively scale.
For 20+ years, DataStage’s parallel engine has exponentially improved ETL/ELT/TETL processing performance, while continuously evolving to meet the changing enterprise needs from distributed to CI/CD to cloud-native architectures. Let’s take a look at the different components of the parallel engine:
- Pipeline parallelism allows for multiple stages of data processing to occur simultaneously, akin to a conveyor belt system. For instance, while one record is being extracted, the next record begins processing immediately, minimizing idle time and reducing the need for extensive disk usage in the staging area. This method ensures that as one record is processed downstream, another is being extracted and prepared upstream, creating a continuous flow of data processing.
- Partition parallelism, on the other hand, divides the data into subsets, or partitions, that are processed independently by different processors. By distributing the workload across multiple processors, partition parallelism enhances processing efficiency and speed, allowing the same operation to be performed simultaneously on different data partitions.
- IBM Databand provides data observability for DataStage and beyond, offering insights into the health and performance of data pipelines across the enterprise. By monitoring and analyzing metadata from data integration jobs, Databand enables data engineers to detect and resolve issues quickly, ensuring that pipelines run efficiently and produce quality output data. Databand makes this possible with the following benefits:
- Comprehensive Insights: Offers detailed insights into the health and performance of data pipelines, ensuring that integration processes are optimized and reliable.
- Proactive Monitoring: Databand scans data integration pipelines and reports on collected metadata, providing proactive alerts on potential pipeline breakages before they occur.
- Enhanced Productivity: By detecting issues early, Databand helps engineers address problems swiftly, increasing their productivity and reducing downtime.
Design once, run anywhere
As data management trends towards hybrid, multi-cloud environments, data integration tools have evolved to support multiple deployment models. The rise of cloud and AI has made fully managed deployments popular, such as IBM DataStage as a Service. While fully managed deployments reduce the administration and infrastructure expense of self-managed deployments, there are still concerns about data sovereignty, cloud security, and performance.
A significant technical development introduced in modernized IBM DataStage is the remote execution engine, which combines the strengths of both fully managed and self-managed models, offering maximum flexibility. Traditionally, ETL/ELT/TETL tools combined the design time and runtime components, but the remote execution engine decouples them. This innovation allows the design time of DataStage to be fully managed on cloud, while the runtime (the DataStage parallel engine) can be deployed in any cloud and any geography.
This innovative flexibility keeps data integration jobs closest to the business data with the customer-managed runtime. It prevents the fully managed design time from touching that data, improving security and performance while retaining the application efficiency benefits of a fully managed model. Whether it’s Amazon EKS, IBM ROKS, Google GKE, or even in traditional data centers, DataStage meets the enterprise where they are today. The ability for IBM DataStage to run ETL/ELT/TETL jobs anywhere reduces data movement, lowers egress costs, minimizes network latency, and boosts pipeline performance while ensuring data security and sovereignty.
Read more about the power of the remote engine here.
Flexible, Reusable Integration Patterns
Building on the remote engine, IBM DataStage allows enterprises to design data pipelines once and pick the target data integration pattern based on their use case at hand without refactoring code or redesigning jobs in the canvas. Simply pick an integration mode (ETL, ELT with SQL pushdown, or TETL) and allow DataStage to compile and run. This streamlined approach eliminates the need for repetitive coding efforts, saving development time and resources.
The flexibility to choose your execution pattern at runtime empowers you to optimize data processing for your specific needs. Let’s delve into the details of each mode:
- ETL (Extract, Transform, Load): This traditional approach extracts data from source systems, transforms it in a staging area, and then loads the transformed data into the target system. In this approach, DataStage’s parallel engine optimizes the transformation process for maximum efficiency. This style is ideal for scenarios requiring complex data transformations before loading into the target system.
- ELT (Extract, Load, Transform): This approach prioritizes speed and efficiency by extracting data from source systems and directly loading it into the target system. The transformation then occurs within the target system itself, leveraging the processing power of the target for transformations that can be expressed in SQL. DataStage translates your data pipeline design into optimized SQL code that executes directly within the target warehouse. This simplifies development, reduces the risk of errors, and leverages the power of your existing database infrastructure. ELT with SQL pushdown is well-suited for large datasets where minimizing data movement and maximizing utilization of warehouse resources are crucial.
- TETL (Transform, Extract, Transform, Load): This approach is opposite of traditional ETL. Data is first transformed within the source system, leveraging its processing capabilities. Then, the transformed data is extracted, potentially transformed again (if needed), and finally loaded into the target system. TELT can be beneficial when the source system has the resources to handle transformations and minimize data transferred for processing.
By offering these flexible integration patterns within a single, reusable design, IBM DataStage empowers you to choose the most efficient approach for your specific data pipeline requirements. This translates to faster development, reduced risk of errors, and ultimately, a modern data integration solution for your enterprise. DataStage, along with the rest of components of IBM’s Data Integration and IBM Data Fabric architecture, enhance the quality of organizations’ data to produce trustworthy outputs. With modernized DataStage, data users can transform raw disparate data into data that is ready for AI, BI, analytics and so much more.