Businesses collect data from disparate sources in a variety of formats. To make sense of this data and extract value out of it, enterprises employ different data integration tools that bring data at one place for transforming and loading to a centralized data repository like a data warehouse or a data mart.
Leveraging different technologies, such as ETL, API integration, data virtualization, EDI and others, data integration makes it easier for businesses to access, move, manipulate, and synchronize data collected from within and outside the enterprise. However, the value garnered from the process depends on the right selection of an enterprise-grade data integration tool that offers the optimal combination of performance, ease of use, and scalability.
What Is Data Integration?
Data Integration is a process of combining data from heterogeneous sources and presenting it in a unified format. This includes:
• Consolidating data from a wide variety of source systems with disparate formats, such as file systems, APIs, databases, etc.
• Cleaning data by removing duplicates, errors, etc.
• Categorizing data based on business rules
• Transforming it into the required format so it can be used for reporting or analysis
Data integration is used in various data management processes like data migration, application integration, master data management, and more. However, now the process of data integration is not only limited to IT users. Due to the availability of easy-to-use data integration tools, more and more business users are taking the lead for optimizing business processes and driving business agility.
Power Your Data Value Chain with Astera’s High-Performance Data Integration Tool
Data integration tools from Astera offer organizations a scalable data management platform to extract data from heterogeneous sources, transform it into a format compatible with their operating and BI systems, and load it to a destination of their choice.
With its easy-to-use interface, out-of-the-box connectivity to a range of modern and legacy data sources, and extensive library of ready-to-use transformations, our data integration solution – Centerprise, delivers a wide range of data quality, profiling, validation, and warehousing capabilities.
Some key features of Centerprise that make it one of the best data integration tools include:
Automated Code Generation
Centerprise frees up developer resources by automating the tedious and mundane task of writing code for complex data extraction and transformation requirements. The data integration software allows users to create dataflows of any complexity in simple drag and drop actions, while it generates SQL queries automatically on the backend.
Support for Bulk and Incremental Data Load
Data integration requirements of businesses vary, depending on the velocity, volume, variety, and veracity of incoming data. For bulk or batch data integration, Centerprise features a high-performance ETL engine with high availability and failover support, allowing business to complete bulk data transfer jobs efficiently. In addition, with support for Change Data Capture (CDC), the data integration tool also allows users to keep data fresh by capturing changed data only, without the need to perform a complete data load.
Connectivity to a Range of Data Sources
To help enterprises gain a unified view of their data assets, the data integration platform offers out-of-the-box connectivity to a range of structured and unstructured data formats, legacy and modern databases, on-premise and cloud applications, and more.
Industrial-Strength ETL Engine with Process Orchestration Support
Centerprise, a top data integration solution employed by Fortune 500 organizations, ensures uninterrupted data delivery to business by utilizing an industrial-strength ETL engine, parallel processing architecture, support for both ETL and ELT, and the ability to orchestrate and automate workflows.
Support for a Variety of Data Management Use Cases
As a robust and versatile data integration tool, Centerprise is built to support a wide variety of data management use cases, including data migrations, data synchronization, data extraction, data warehousing, data conversion, and data manipulation for BI and data analytics tools.