Did you know, data integration is the number one challenge that companies are facing for digital transformation? That’s because there are so many data sources, new data types, and hybrid cloud and on-premises environments, all presenting serious challenges.
The good news is that businesses that need data integration tools have a wide variety of alternatives at their fingertips. But, how can you browse through so many data integration options to pick the right one for your requirements and goals?
Explore the top 9 data integration features that you should look for when choosing a solution for your company.
Top 9 Must-Have Data Integration Features
Data integration tools have the potential to simplify the ETL process a great deal. Look for these features in a data integration tool:
1. Automation and Job Scheduling
Large enterprises handle hundreds of integration jobs every day. The more you can automate these tasks, the faster and easier it’ll be for you to extract insights from data. A data integration tool should support built-in job scheduling and automation so that you can schedule anything from a simple data transformation job to a complex workflow comprising several subflows. It should allow you to leverage process orchestration capabilities so that you can sequence integration and transformation jobs, which can be executed serially or in parallel on several servers. Moreover, it should support SQL execution, external program execution, FTP uploads and downloads, and email.
Running a job scheduler in Astera Centerprise.
2. Pushdown Optimization
A data integration tool should offer both ELT and ETL operations. In case of ELT operation, you should be able to push down a data transformation job into a relational database, where needed. This will allow you to make better use of database resources and enhance performance. As a result, you can better manage processing needs, save more time, and increase developer productivity.
3. Number of Connectors
A data integration tool should have a wide range of built-in connectors for both modern and conventional data sources. This is critical because one of the main jobs of a data integration tool is to enable bi-directional movement of data between a wide variety of internal and external data sources that an enterprise uses.You should be able to connect it with simple CSV, Excel, or fixed-length files, relational databases, hierarchical EDI and XML files, legacy formats, enterprise applications, cloud solutions, data warehouses, and more.
A data integration tool should have a parallel processing engine so that you can run data transformation jobs in parallel. As a result, the entire dataflow, or parts of it, can be processed in parallel on several nodes. And, each node can have a part of the dataflows. This means you can experience unparalleled performance even when processing huge datasets.
5. Data Quality, Profiling, and Validation
A data integration tool should have built-in data profiling features so that you can easily examine your source data and obtain detailed info about its structure, quality, and integrity. It should also allow you to define custom data quality rules to validate received data and identify missing or invalid records.
6. Drag-and-Drop GUI
Ideally, a data integration tool should have a visual, drag-and-drop interface so that your non-tech savvy users can also operate it easily. It should provide advanced-level functionality for development, debugging, and testing in a code-free environment. Plus, the tool should offer the same level of usability to both developers and enterprises with accessible functionalities such as instant data preview, in-built transformations, and native connectivity to several data sources.
7. On-premise and Cloud
The data integration tool you choose should be capable of being quickly deployed on-premise or in the cloud. You should be able to containerize the application. A container is a kind of an operating system virtualization which helps in the efficient running of applications across platforms with minimum resource consumption.Containerization enables speedy deployment, patching, and scaling of applications.
8. Support for Legacy Systems
Legacy systems are old, inflexible technologies put into place to solve previous business challenges. These systems, due to their long lifespans, tend to be inflexible, expensive to maintain, and difficult to integrate with new cloud and web-based services. Your data integration tool should be able to integrate with legacy systems so that you can unlock the value of your old systems. It should have suitable connectors (such as COBOL, IBM DB2, Netezza, Sybase, etc.) allowing for connecting with legacy systems on-premises or in the cloud quickly.
9. Pre-built Transformations
A data integration tool should simplify the process of transforming complex hierarchical data with its extensive range of built-in transformations. These transformations should allow you to create a complete dataflow and automate using built-in job scheduling and automation features.
Business intelligence, analytics, and competitive advantages are all at stake when it comes to data integration. That’s why your company must have full access to every data set from every source. Using a robust data integration tool like Astera Centerprise that has all these features can help your business consolidate data from virtually any source and prepare it for analysis with any data warehouse.
Data integration is fundamental to constructing a consolidated, reliable data warehouse. Are you looking for a powerful tool to integrate all your enterprise data? Check out the free trial of Astera Centerprise!