Databases Overview
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Databases Overview

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Rivery enables migrating data from legacy and cloud databases to one of the DWHs we support.
Our database connectors are built with correctness, safety, and security in mind. The term "correctness" refers to the fact that your destination should always be an exact copy of your database source. Rivery should never cause difficulties in your source database, and clients can use a custom file zone to ensure that data does not leave their environment during transit.

We have simple methods to obtain data from databases without needing access to Binary Logs, in addition to connecting with Binary Log capture tools. We can handle more customers and databases thanks to our customizable approach.

Compatibility of Versions

DatabaseChange Data Capture MethodsVersions SupportedSupport for Managing Schema Drift
ElasticSearchStandard ExtractionScroll API Version 7.10
Indexes API Version 7.10
Mapping API Version 7.10
MariaDBStandard ExtractionAll Versions
Log Based
System Versioned Tables
Microsoft SQL ServerStandard ExtractionAll Versions
Change Data CaptureSp1 2016 and Above (Standard or Enterprise Editions)
Change TrackingAll Versions
MongoDBStandard Extraction
Versions 3.4 and Above
Change Streams Versions 4.0 and Above
MySQLStandard ExtractionAll Versions
Log BasedMySQL 5.6 and Above, or MySQL 8.0 and Above
OracleStandard Extraction
Versions 11.x and Above
PostgreSQLStandard ExtractionAll Versions
Write Ahead Logging (WAL)Versions 10 and Above
RedshiftStandard Extraction
All Versions
SAP S4/HanaStandard ExtractionVersions 2020 and Above
TeradataStandard Extraction
Version 15.10
VerticaStandard ExtractionAll Versions

Parquet File Conversion

It is possible to convert CSV/JSON files to Parquet files when using Amazon S3 as the Target.

Consult our Quick Guide for an illustration of how to do it.

Please keep in mind that while the Quick Guide uses Amazon S3 as a Source, this is possible with any Database.

Incremental Behavior

Rivery works in millisecond increments, but users can store data in more granular microsecond or nanosecond increments.

When operating incremental runs based on timestamp, it may appear that the most recent record is missing.

Rivery reads three numbers after the dot when there are six, so '2022-08-14 10:26:52.895132' becomes '2022-08-14 10:26:52.895' and the last record is missing from the current run when fetching the data.

Please note that this record will be retrieved in the next run with updated data.

Soft Delete

A "Soft Delete" is a method of deleting data from a database table while still maintaining a record of the deleted data. This is typically done by adding a "__deleted" flag to the rows of data that are marked for deletion, rather than physically deleting the rows from the table.

Using CDC, this process can be automated and tracked in real-time, allowing for a more efficient and accurate way to manage data deletion and updates. It also allows for the possibility of "undeleting" data if needed, as the deleted rows are still present in the table with the "__deleted" flag set to true.

Here is an example of how you might use the DELETE statement: If you have a Source table, and you use the DELETE statement in your database to mark a row with an ID of 22 as deleted, the outcome of the Target table will be as follows:

Please note:

Soft Delete does not actually delete the record from the table, so it will still take up space in the database and will be included in backups unless you take additional steps to exclude it.

Connection

Click the sections below for details on how to connect to our supported databases:

ElasticSearch

MariaDB

Microsoft SQL Server

MongoDB

MySQL

Oracle

PostgreSQL

Redshift

SAP S4/Hana

Teradata

Vertica



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