Oracle Databricks CDC
in Real-time.


Connect Oracle Database to Databricks, load with CDC

Automate Oracle Databricks Migration using BryteFlow

Connecting Oracle Database to Databricks Lakehouse becomes easy and automated with BryteFlow. When you ETL Oracle data to Databricks with BryteFlow, you can avoid coding and load your data in real-time to Databricks on AWS or Azure using CDC (Change Data Capture). Access ready -for-consumption data in your Databricks Lakehouse on AWS or Azure. BryteFlow is fast to deploy and you can start getting delivery of data in just 2 weeks. Databricks Lakehouse and Delta Lake (A Dynamic Duo!)

Oracle Replication with BryteFlow

No-Code Real-time Oracle CDC to Databricks

BryteFlow uses log-based CDC to replicate from Oracle database to Databricks. It connects Oracle to Databricks in real-time and transfers high volume data rapidly with parallel, multi-thread loading, partitioning and compression for the initial full refresh. It ingests incremental data using log-based CDC and creates tables automatically on Databricks (AWS and Azure) so you can avoid tedious data prep. It also provides time versioning of data with SCD Type2 data and a history of every transaction. Oracle CDC: 13 Things to Know

Real-time Oracle to Databricks ETL and Integration

Suggested Reading:
ETL Pipelines and 6 Reasons to Automate Them
Oracle to SQL Server Migration: Reasons, Challenges and Tools
Oracle Replication in Real Time, Step by Step
Oracle to Snowflake: Everything You Need to Know

No-Code Replication from Oracle to Databricks on AWS and Azure

The BryteFlow data replication tool replicates huge volumes of enterprise data from Oracle to Databricks

BryteFlow XL Ingest loads the initial full refresh of data from Oracle to Databricks using parallel multi-thread loading, smart partitioning and compression. Incremental data is transferred by BryteFlow Ingest using log-based Change Data Capture to sync data with changes at source. All deltas including Inserts, Updates and Deletes are mergd automatically with existing data.
Connect Oracle to Databricks and Load Data the Easy Way

Oracle to Databricks Migration is completely automated

Many Oracle data tools involve connectors and pipelines to move your Oracle data to Databricks and there is usually some coding involved, for e.g. to merge data for basic Oracle CDC. With BryteFlow there is no coding. The simple, user-friendly interface can be used easily by ordinary business users.
Databricks Lakehouse and Delta Lake (A Dynamic Duo!)

Our data integration software uses efficient Oracle CDC to update data in your Databricks Lakehouse

BryteFlow updates data on Databricks with changes at source using log-based Change Data Capture which is zero impact and uses database transaction logs to query Oracle data at source. It copies only the changes into the Databricks Lakehouse. The data in the Databricks Lakehouse is updated in real-time or at a frequency of your choosing.
Oracle CDC (Change Data Capture): 13 Things to Know

Cut down time spent by Database Administrators in managing the replication

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 SQL Server Migration: Reasons, Challenges and Tools

Data from Oracle to Databricks is monitored for data completeness from start to finish

BryteFlow provides end-to-end monitoring and tracks your data. For e.g. if you are replicating data from Oracle to Databricks at 2pm on Friday, Sep 2. 2022, all the changes that took place till that point will be replicated to the Databricks Lakehouse, latest change last so the data will be replicated with all inserts, deletes and changes present at source at that point in time. For data ingest, BryteFlow ControlRoom will display the latency, operation start time, operation end time, volume of data ingested and data remaining.
Oracle to Snowflake: Everything You Need to Know

Your Data maintains Referential Integrity

With BryteFlow you can maintain the referential integrity of your data when replicating Oracle data to Databricks on AWS or Azure. This means when there are changes in the Oracle source and when those changes are replicated to the destination (Databricks) you can pinpoint what changed, including the date, time and values that changed at the columnar level.
AWS DMS CDC and Limitations for Oracle Sources

Data replication 6x faster than Oracle GoldenGate

BryteFlow has very high throughput of approx. 1,000,000 rows in 30 seconds and is at least 6 x faster than GoldenGate. This is based on actual experience with a client in a product trial. Try out BryteFlow for yourself and see how fast it works to migrate your Oracle data to Databricks. BryteFlow can be deployed fast and you can get delivery of data in just 2 weeks.
Oracle GoldenGate CDC and a better alternative

Option for Remote Log Mining of Oracle Data

BryteFlow enables remote log mining for Oracle data. The logs can be mined on a completely different server therefore there is no load on the source. Your operational systems and sources are never impacted even though you may be mining very large volumes of data.
Oracle Database Replication with BryteFlow

Automated Catch-up from Network Dropout

If the data replication is interrupted by a power outage or network failure, you don’t need to start the process of replicating Oracle data to Databricks over again. BryteFlow automatically picks up where it left off, when normal conditions are restored.

Technical Architecture on Databricks

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 Databricks

Databricks is a unified, cloud-based platform that handles multiple data objectives ranging from data science, machine learning and analytics to data engineering , reporting and BI. The Databricks Lakehouse simplifies data access since a single system can handle both- affordable data storage (like a data lake) and analytical capabilities (like a data warehouse). Databricks can be implemented on Cloud platforms like AWS and Azure and is immensely scalable and fast. It also enables collaboration between users.