Oracle CDC to Kafka without coding.

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Easy Oracle CDC to Kafka

Oracle Kafka Streaming is Real-time and No-code with BryteFlow.

Oracle CDC to Kafka with Bryteflow is automated and real-time. BryteFlow’s Oracle Change Data Capture delivers real-time data streams to your Apache Kafka platform. The Inserts, Deletes and Updates in the Oracle database are delivered in real-time to Kafka. BryteFlow’s log-based CDC to Kafka uses Oracle logs to capture changes at source and has almost no impact on source systems. Since only the changed data is loaded, the network bandwidth is used optimally. BryteFlow’s Oracle Kafka CDC delivers granular, high quality, reliable data.
Apache Kafka Overview and how to install it

BryteFlow as an Oracle Kafka Connector

  • BryteFlow delivers data from sources to Kafka with extremely low latency, using log-based CDC after the first full one time load into Topics. Every insert, update and delete in the Oracle database is captured and delivered in near real-time to Kafka. About BryteFlow Ingest, our data replication tool
  • Log-based CDC from Oracle to Kafka ensures there is no impact on Oracle source systems.
  • Besides Oracle, BryteFlow enables real-time streaming from your relational databases like SAP, SQL Server, Postgres, and MySQLto Kafka
  • BryteFlow provides automated partitioning and compression for efficient replication of data to Kafka
  • With BryteFlow you get the highest throughput for Oracle CDC to Kafka with parallel extraction and loading which is completely configurable. Oracle CDC (Change Data Capture): 13 things to know
  • BryteFlow delivers data in the JSON format to Kafka.
  • It supports real-time data replication from all versions of Oracle including Oracle 12c, Oracle 19c and the replication is at least 6x faster than Oracle Goldengate. Real-time Oracle Replication step by step
  • BryteFlow populates the schema registry automatically
  • Easy to use, graphical point-and-click interface enables automated data flows, alerts and easy monitoring of data with zero coding.
Learn about BryteFlow for Oracle CDC

Real-time, No-code, Oracle to Kafka CDC

The BryteFlow replication tool loads large volumes of streaming Oracle data to Kafka really fast

BryteFlow Ingest uses Change Data Capture for Oracle Kafka replication, delivering changes at source in real-time to your Kafka platform at a speed of approx. 1000,000 rows in 30 seconds.
Kafka CDC and Integration

Populating the Schema Registry

The Schema Registry serves as a distributed storage layer for schemas using Kafka as the underlying storage platform. BryteFlow enables automatic population of the Schema Registry without manual coding.

Oracle CDC to Kafka has zero impact on source systems

BryteFlow’s Oracle CDC to Kafka is zero impact and uses database transaction logs to query Oracle data at source and copies only the changes into Apache Kafka. The Kafka data is updated in real-time or at a frequency of your choosing, automatically .
Oracle CDC (Change Data Capture): 13 things to Know

How much time do your Database Administrators need to spend on managing Oracle Kafka CDC?

You need to work out how much time your DBAs will need to spend on the solution, in managing backups, managing dependencies until the changes have been processed, in configuring full backups and then work out the true Total Cost of Ownership (TCO) of the solution. The replication user in most of these replication scenarios needs to have the highest sysadmin privileges.

With BryteFlow, it is “set and forget”. There is no involvement from the DBAs required on a continual basis, hence the TCO is much lower. Further, you do not need sysadmin privileges for the replication user.

Oracle to Kafka CDC replication is completely automated

Most Oracle Kafka replication software will set up connectors to stream your Oracle data to Kafka but there is usually coding involved at some point for e.g. to merge data for basic Oracle CDC. With BryteFlow you never face any of those annoyances. Oracle data replication, CDC to Kafka, data merges etc. are all automated and self-service with a point and click interface that ordinary business users can use with ease.
Oracle to SQL Server Migration: Reasons, Challenges and Tools

Data loading from Oracle to Kafka is monitored continuously

BryteFlow provides end-to-end monitoring of data. Reliability is our strong focus as the success of the analytics projects depends on this reliability. Unlike other software which set up connectors and pipelines to Oracle source applications and stream your data without checking the data accuracy or completeness, BryteFlow makes it a point to track your data from start to finish.
BryteFlow for Kafka CDC and Integration

Remote log mining possible with the software

With BryteFlow you can use remote log mining. The logs can be mined on a completely different server therefore there is zero load on the source. Your operational systems and sources are never impacted even though you may be mining huge volumes of data.

Data replication tool faster than Oracle GoldenGate

BryteFlow replication of Oracle data is the fastest that we know of. It is at least 6x faster than Goldengate. Average speed is 1,000,000 rows ler second. BryteFlow can be deployed in one day and you can start getting delivery of data in two weeks.
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Data gets automatic catch-up from network dropout

If there is a power outage or network failure you don’t need to start the Oracle to Kafka CDC replication over again. Yes, with most software but not with BryteFlow. You can simply pick up where you left off – automatically.

Secure encryption of data

BryteFlow enables automated secure encryption of your data in transit and at rest. This is particularly useful in case of sensitive information.
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Kafka as a target and Kafka as a source

BryteFlow functions as a Kafka connector and replication tool delivering data from sources to Kafka and delivering data from Kafka to targets like Snowflake, Amazon S3, Redshift, SQL Server, Azure Synapse and ADLS Gen2. BryteFlow treats Kafka as a target and as a source for data replication.
Kafka CDC and Integration with BryteFlow

BryteFlow’s Technical Architecture

BryteFlow’s Data Integration Tools

The BryteFlow software consists of data integration tools that work synergistically to deliver flawlessly
replicated, prepared data that you can use for your Analytics, ML, AI or other applications.
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BryteFlow Ingest: our data replication tool

The data replication superstar, replicates data from any file, database or API.
About BryteFlow Ingest 

BryteFlow XL Ingest for data replication of very large datasets

Specially designed to replicate tables over 50 GB fast and seamlessly.
About BryteFlow XL Ingest

BryteFlow Blend: our data transformation tool

Merges data from different sources and prepares it for Analytics, Machine Learning etc.
About BryteFlow Blend

BryteFlow TruData: our data reconciliation tool

Ensures completeness of data including Type2, issues alerts if data is missing.
About BryteFlow TruData

BryteFlow ControlRoom: our data monitoring tool

The Bryteflow ControlRoom is an operational dashboard that monitors all instances of BryteFlow Ingest and BryteFlow Blend, displaying the statuses of various replication and transform instances.
About BryteFlow ControlRoom

About Oracle Database

Oracle DB is also known as Oracle RDBMS (Relational Database Management System) and sometimes just Oracle. Oracle DB allows users to directly access a relational database framework and its data objects through SQL (Structured Query Language). Oracle is highly scalable and is used by global organizations to manage and process data across local and wide area networks. The Oracle database allows communication across networks through its proprietary network component.

About Apache Kafka

Apache Kafka is a distributed messaging platform created to manage real-time data ingestion and processing of streaming data. Kafka builds real-time streaming data pipelines and real-time streaming applications. Kafka servers or brokers consume and process high volumes of streaming records from millions of events per day combining queuing and publish-subscribe messaging models for distributed data processing. This allows data processing to be carried out across multiple consumer instances and enables every subscriber to receive every message. More on Apache Kafka