Move data from Oracle to S3
The fastest and easiest way to replicate Oracle data to Amazon S3
Need to replicate Oracle data to Amazon S3? Thinking of which Oracle replication tool to use? There are many automated data replication tools out there that can supposedly ETL your Oracle data to Amazon S3 fast. However you may need to consider certain points that you may have overlooked. AWS DMS Limitations for Oracle Replication
Learn about BryteFlow for Oracle
Why migrate Oracle data to S3 with BryteFlow
- Low impact, low latency transactional log replication from Oracle to Amazon S3. Oracle Replication in Real-time, step by step
- BryteFlow automatically optimises S3 with columnar file formats, partitioning and compression. Build a Data Lakehouse on Amazon S3
- BryteFlow integrates seamlessly with AWS Athena, Lake Formation, Glue Data Catalog and Redshift Spectrum to make them run 15x faster. AWS ETL with BryteFlow
- Save on data integration costs by 25 times*. An S3 Data Lake with BryteFlow (video tutorial)
- BryteFlow provides replication support for all Oracle versions, including Oracle 12c, 19c, 21c and future releases for the long term.
*Results based on trials conducted at a client site
Real-time, codeless, automated Oracle data replication to Amazon S3
Can your replication tool replicate really, really large volumes of Oracle data to your Amazon S3 data lake fast?
When your data tables are true Godzillas, including Oracle data, most data replication software roll over and die. Not BryteFlow. It tackles terabytes of data for Oracle replication head-on. BryteFlow XL Ingest has been specially created to replicate huge amounts of Oracle data to Amazon S3 at super-fast speeds.
Secrets to Fast Bulk Loading of Data to Cloud Data Warehouses
Access Operational Metadata out of the box
BryteFlow keeps operational metadata out of the box of all the extraction and load processes. This can be saved on Aurora if required. The metadata includes currency of data and data lineage. Currency of data shows the status of the data whether it is active, archived, or purged. Data lineage represents the history of the migrated data and transformation applied on it.
Prepare data on Amazon S3 and copy to Amazon Redshift or use Redshift Spectrum to query data on Amazon S3
BryteFlow provides the option of preparing data on S3 and to copy it to Redshift for complex querying. Or you can use Redshift Spectrum to query the data on S3 without actually loading it onto Amazon Redshift. This distributes the data processing load over S3 and Redshift saving hugely on processing and storage cost and time. Build an S3 Data Lake in Minutes
How much time do your Database Administrators need to spend on 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. S3 Security Best Practices
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.
Are you sure Oracle replication to Amazon S3 and transformation are completely automated?
This is a big one. Most Oracle data tools will set up connectors and pipelines to stream your Oracle data to S3 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, data merges, SCD Type2 history, data transformation and data reconciliation are all automated and self-service with a point and click interface that ordinary business users can use with ease.
Is your data from Oracle to Amazon S3 monitored for data completeness from start to finish?
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. For e.g. if you are replicating Oracle data to S3 at 2pm on Thursday, Nov. 2019, all the changes that happened till that point will be replicated to the Amazon S3 database, latest change last so the data will be replicated with all inserts, deletes and changes present at source at that point in time.
Oracle Replication in Real-time, step by step
Does your data integration software use time-consuming ETL or efficient Oracle CDC to replicate changes?
Very often software depends on a full refresh to update destination data with changes at source. This is time consuming and affects source systems negatively, impacting productivity and performance. BryteFlow uses Oracle CDC to S3 which is zero impact and uses database transaction logs to query Oracle data at source and copies only the changes into the Amazon S3 database. The data in the S3 data lake is updated in real-time or at a frequency of your choice. Log based Oracle CDC is absolutely the fastest, most efficient way to replicate your Oracle data to the Amazon S3 data lake. Learn more about Oracle CDC (Change Data Capture)
Does your data maintain Referential Integrity?
With BryteFlow you can maintain the referential integrity of your data when replicating Oracle data to AWS S3. What does this mean? Simply put, it means when there are changes in the Oracle source and when those changes are replicated to the destination (S3) you can put your finger exactly on the date, the time and the values that changed at the columnar level.
Is your data automatically reconciled in the S3 data lake?
With BryteFlow, data in the S3 data lake is validated against data in the Oracle replication database continually or you can choose a frequency for this to happen. It performs point-in-time data completeness checks for complete datasets including type-2. It compares row counts and columns checksum in the Oracle replication database and S3 data at a very granular level.Very few data integration software provide this feature.
Do you have the option to archive data while preserving SCD Type 2 history?
BryteFlow does. It provides time-stamped data and the versioning feature allows you to retrieve data from any point on the timeline. This versioning feature is a ‘must have’ for historical and predictive trend analysis.
Can your data get automatic catch-up from network dropout?
If there is a power outage or network failure will you need to start the Oracle data replication to S3 process over again? Yes, with most software but not with BryteFlow. You can simply pick up where you left off – automatically.
Can your Oracle data be merged with data from other sources?
With BryteFlow you can merge any kind of data from multiple sources with your data from Oracle for Analytics or Machine Learning.
More on Build an S3 Data Lake
Is 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.
Is the data replication tool faster than Oracle GoldenGate?
BryteFlow replication of Oracle data definitely is. This is based on actual experience with a client and not an idle boast. Try out BryteFlow for yourself and see exactly how fast it works to migrate your Oracle data to S3.
Get a Free Trial of BryteFlow
Load data fast with smart partitoning and compression
BryteFlow Ingest provides parallel sync at the initial ingest of data and compresses and partitions data so it can be loaded extremely fast. This has minimal impact on source and the Oracle data replication proceeds smoothly. Even in the case that your data replication is interrupted by a network outage, your data replication just starts from the last partition that was being ingested instead of the beginning.
Since BryteFlow Ingest compresses and stores data on Amazon S3 in smart partitions you can run queries very fast even with many other users running queries concurrently. It eliminates heavy batch processing, so your users can access current data, even from heavy loaded EDWs or Transactional Systems.
BryteFlow interfaces seamlessly with AWS Lake Formation and Glue Data Catalog for optimal functioning
BryteFlow interfaces seamlessly with AWS Lake Formation and adds automation to the mix so you can deploy an S3 data lake 10x faster while taking advantage of everything AWS Lake Formation has to offer, including finer grain access control.
BryteFlow also interfaces directly with the Glue Data Catalog via API. Information in the Glue Data Catalog is stored as metadata tables and helps with ETL processing. BryteFlow enables automated partitioning of tables and automated populating of the Glue Data Catalog with metadata so you can bypass laborious coding and extract and query data faster.
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 Amazon S3
Amazon S3 or Amazon Simple Storage Service is an object storage service that is scalable, flexible, always available and highly secure. It can be used by all kinds of industries to store petabytes of data. Data on S3 is stored in S3 buckets and can be used in many applications including websites, mobile apps, IoT devices, enterprise applications and big data analytics. Companies can build highly durable data lakes on Amazon S3 and organize data as per requirement for storage or analytics.