Move, replicate, and transform data without writing code
Data Ingester is one pipeline engine with three modes. X→Y moves data between any source and any destination in minutes, Change Data Capture replicates databases at scale with low latency, and Advance ETL runs the most intricate transformations on Apache Spark. All no-code, all governed, across 2000+ connectors.
Data Ingester: Connect, Stream & Scale Enterprise Data
Build AI-ready pipelines with X→Y movement, Change Data Capture, and advanced ETL across databases, cloud, APIs, and streaming sources.
Move data from any source to any destination with X→Y pipelines
Point at a source, point at a destination, and the pipeline runs. Standing one up takes minutes and no code, which is the difference between a request being answered this week and being added to a backlog.
No-code interface
Pipelines are built and managed through a no-code interface, so the person who understands the data can build the pipeline for it. Getting data where it needs to be stops depending on who has capacity this sprint.
Move data between any source and destination within minutes. Whether working with data stores, cloud databases, or data warehouses, DataByte adapts to the unique needs of.
Replicate databases at large scale with low latency
Changes in the source system are tracked and replicated as they happen, so the downstream view is current to the minute rather than to last night's batch.
Efficient & consistent data replication
Change data capture keeps systems in step without re-reading whole tables. Only what changed moves, which is why it costs a fraction of a nightly full load and why the target is minutes behind the source rather than a day.
Experience speed & scale in complex data transformations
Sources rarely agree on format, and the transformation rules get complicated fast. Pipelines carry data from source to destination and hand it over in the shape the consuming system actually expects.
Automated data transformations
Data arriving from several connectors can be transformed in the same flow that ingested it. ETL and ELT pipelines are built through a no-code interface and land data in the shape the consuming system expects, whether that is a report, a model, or another application.
Works with the rest of the platform.
Connect any two systems without a custom project. Build the flow on a visual canvas, test it before it goes live, and run it with approvals attached.
Read moreBuild transformation pipelines on a visual canvas or in a notebook, whichever suits the person doing the work. One engine for batch, streaming, and on demand.
Read moreTake a model from data preparation to production in one place. AutoML and experiment tracking, one-click deployment as a versioned API, drift monitoring after go-live.
Read moreGet your data moving in minutes
See Data Ingester build an X→Y pipeline, a CDC flow, and a Spark transformation against your own sources, live, on your stack.