Enterprises ingesting on-premise data to clouds like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP) – or a replication of data between clouds – are familiar with an array of challenges. Ingesting data from on-premise to a cloud-based data warehouse typically involves large data volumes with complex transformation required. Traditional ETL only supports batch updates and can buckle under heavy data loads. Implementing data streaming can solve these challenges.
Equalum supports cloud ingestion by extracting real-time or batch changes in on-premise data and sending directly to the cloud. Equalum’s zero-coding approach enables rapid setup of data pipelines, with no custom development required – and without the need for dedicated cloud-specific data transfer tools (e.g., AWS Snowball, Azure Data Transfers) for each cloud instance. Equalum uniquely combines its native technology with Apache Spark and Kafka to provide unmatched performance at any scale. Equalum also provides change data capture (CDC) capabilities enabling non-intrusive, seamless, near real-time ingestion.
Replicate on-premise data to the cloud in real-time or batch
Leveraging Spark and Kafka for scalability; supports streaming or batch transfer between any number of on-premise data sources and multi-cloud targets.
Change Data Capture Extraction Approach
Breakthrough use of CDC creates minimal system strain and allows capture of changes from live applications after syncing historical endpoints.
Build data pipelines in minutes
Zero-coding approach; plug-and-play integration with on-premise data stores and cloud platforms.
A global manufacturer reduced the cost of development associated with their cloud ingestion by 70% using Equalum’s technology to replicate data between on-premise systems and their MS Azure data lake, which powers the enterprise’s real-time analytics environment.
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