ETL Tools

Extract, transform, and load (ETL) tools help organizations consolidate and integrate their data into physical repositories like data warehouses and marts.

What are ETL tools? ETL tools are a specialized form of software that allow any organization to extract data from numerous disparate databases, applications and systems, transform the data into a usable format, and load the data from all of these sources into a single database, data mart, or data warehouse for reporting, analysis, and data synchronization. ETL tools are particularly useful at larger companies and organizations which may have accumulated a diverse assortment of data sources over time either organically or through mergers and acquisitions.

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DataMigrator is a powerful automated ETL tool that simplifies extract, transformation, and load processes, including the creation, maintenance, and expansion of data warehouses, data marts, and operational data stores. Other tools, such as iWay Big Data Integrator and Omni-Gen, enable modern data management using extract-load-transform, and other data integration methods.

ETL and other traditional methods of data integration are rapidly changing to adapt to increasingly complex and vast data architectures. For example, ELT, which stands for “extract, load, and transform”, may be better suited for big data scenarios than ETL tools because it takes strain off of the operational systems that act as sources.  

iWay Big Data Integrator enables ELT and simplifies the creation, management, and use of Hadoop-based data lakes. It provides a modern, native approach to Hadoop-based data integration and management that ensures high levels of capability, compatibility, and flexibility to help your organization.

Extract, transform, and load processes are often converted to extract, load, and transform (ELT) processes in Hadoop

iWay Big Data Integrator provides a simple interface for Hadoop and Spark data ingestion, transformation, changed data capture (CDC), enrichment, and cleansing of big data sources. It can ingest data from structured relational data sources (RDBMS), defined Hadoop, and other NoSQL sources and file formats (Hive, Avro, sequence files, Snappy-compressed CSV, etc.). It can also capture and handle event or streaming events sent via Apache Kafka, Flume, and Spark streaming to enrich, or combine or cleanse event data.

Additionally, data mastering may be more suitable than extract, transform, and load tools for situations that require access to complete, consistent data in real time. Omni-Gen Master Data Management Edition provides seamless integration and the ability to interact with existing systems and data structures to enable the rapid consolidation of millions of records according to easily defined business rules. Unified and validated master data is instantly available to a wide range of enterprise applications, such as enterprise resource planning (ERP), customer relationship management (CRM), self-service portals, analytical tools, data warehouses, and other internal systems.

Data integration may involve any combination of ETL, ELT, and data management processes such as data mastering

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