Stream data from any file type (e.g., XML, CSVs, JSON) to enterprise data warehouses and data lakes to power real-time analytics.
With the current proliferation of file formats throughout the enterprise (e.g., point of sale data in XMLs, API responses in JSON, network data in log files), centralizing data into a data warehouse or data lake can be challenging . Homegrown scripts or MapReduce-based ETL processes are bug-prone, require custom integration for every file type, and fail under high data volumes. And traditional ETL solutions, in addition to only supporting batch updates, don’t efficiently handle complex schema (for example, an XML that references data elements distributed across multiple database tables).
Equalum streams data from any file format to your data warehouse or data lake – the instant it’s created – enabling teams to correlate data sources from across the enterprise for real-time insight.
Support for complex files and schema (e.g., lookups that would require referencing multiple database tables); ability to preview data while developing workflows, breakthrough use of CDC to capture new files or changes within a file.
Leveraging a Spark and Kafka foundation; Equalum provides blazing fast data ingestion between any number of sources and targets in real-time while processing multiple files with massive parallelism.
Eliminates complex ETL programming and scripting with a zero-coding approach and large number of predefined functions for transformations and manipulations.
Provides the best-in-class security, monitoring, fault tolerance, and availability.
A Fortune 100 OEM captures hardware issues for clients in complex XML files, each of which contains information on business entities distributed across multiple database tables. The team transitioned from a MapReduce-based ETL process to Equalum to accelerate their new client onboarding flow and ensure accurate load of XML-based data to their EDW.