Discover how a bimodal integration strategy can address the major data management challenges facing your organization today.
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Automated Replication to popular Databases, Data Lakes, & Data Warehouses
- Straightforward automated data replication.
- Synchronize data with 100+ destinations.
- Easy-to-use ETL/ELT data movement.
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Adobe Analytics is one of more than 200 data sources included in CData Sync [learn more].
CData Sync provides a straightforward way to continuously pipeline your Adobe Analytics data to any Database, Data Lake, or Data Warehouse, making it easily available to Analytics, Reporting, AI, and Machine Learning.
- Synchronize data with a wide range of traditional and emerging databases.
- Replicate Adobe Analytics to RDBMS systems to facilitate operational reporting.
- Offload queries from operational systems to reduce load and increase performance.
- Connect Adobe Analytics to analytics for BI and decision support.
- Archive data for disaster recovery.
Automated Continuous Data Replication
CData Sync provides users with a straightforward way to synchronize data from Adobe Analytics with a wide range of traditional and emerging databases including:
Amazon Redshift Amazon S3 Apache Cassandra Apache Kafka Azure Data Lake Azure Synapse Couchbase CSV Databricks Google AlloyDB Google BigQuery Google Cloud SQL Heroku / Salesforce Connect IBM DB2 Microsoft Access Microsoft Azure Tables MongoDB MySQL Oracle Database PostgreSQL SAP HANA SingleStore Snowflake SQL Server SQLite VerticaKey Features
Replicate Any Data Source To Any Database With a Few Clicks
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Simple Point-And-Click Replication
Configuring replication is easy: Login to the application, select the tables of data to replicate, and select a replication interval. Done.
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Heterogeneous Support & Database Independence
Supports replication across a wide range of commonly used databases, ensuring offline replication without the need to setup and maintain a new or foreign DBMS. In addition, database connectivity is extensible, providing systems integrators with the ability to synchronize with entirely new data stores.
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Automated Iterative Data Extraction
CData Sync extracts data iteratively, causing minimal impact on operational systems by only querying and updating data that has been added or changed since the last update.
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Full or Partial Data Replication
Users have complete control to define which data should be replicated and how that data should be replicated to a database. CData Sync offers the utmost in flexibility across full and partial replication scenarios.
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Secure Backup & Archiving
Protect your organization from the loss of valuable data. CData Sync ensures that your critical data is stored safely in the on-premise or cloud database of choice.
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DBA-Friendly Setup
CData Sync is completely self-contained. As a result it does not require special administration or installation on target databases.
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Flexible Mapping, Configuring, & Scheduling
CData Sync is incredibly simple, yet highly customizable. Almost every facet of the application can be customized to integrate with the user's ideal replication configuration.
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Unix Cron-Style Scheduling
CData Sync includes flexible scheduling capabilities. Users can easily schedule replication tasks at customizable intervals.
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Dynamic Schema Replication
Replicates schema changes dynamically, ensuring that data sources are in sync. CData Sync monitors changes in the connected datasource and the changes are automatically detected and updated within the local replicated database.
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Advanced Logging & Transaction Monitoring
Transaction logging is available to enable closer monitoring of changes being made to replicated data and data accessed directly through CData Sync.
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On-Premise Or In The Cloud
CData Sync is a go-anywhere application that is designed to perform equally well hosted on-premise (Windows, Unix / Linux, Mac OS) or in the Cloud (Azure, Amazon, Google).
Frequently Asked Adobe Analytics Data Integration Questions
Learn more about using CData Sync to replicate Adobe Analytics
What is Adobe Analytics Data Integration?
How do I connect Adobe Analytics to Snowflake?
Connecting Adobe Analytics to Snowflake can involve various integration methods to transfer and synchronize data between the two platforms. While it is possible to build custom integrations through APIs, the most common process involves using an enterprise-class data integration tool like CData Sync to automatically and continuously replicate Adobe Analytics data to Snowflake. With CData Sync, users can easily deploy robust data pipelines that create and maintain a replica of Adobe Analytics data to Snowflake, or any other cloud database or data warehouse.
How do I move Adobe Analytics data to SQL Server?
Unlike replicating Adobe Analytics to a cloud data warehouse or data lake where data is commonly moved first and transformed at the destination via an ELT process, relational databases like SQL Server require ETL and in-flight data transformation. There are many ways to build ETL processes that integrate Adobe Analytics data with SQL Server. We recommend CData Sync, an easy-to-use data pipeline tool that helps users automatically and continuously replicate Adobe Analytics data to any database or data warehouse, including SQL Server.
Can I replicate Adobe Analytics to NoSQL databases?
Yes, CData Sync is an any-to-any data pipeline tool that supports data replication between Adobe Analytics and any database or data warehouse, including NoSQL databases. The transformation capabilities of CData Sync make it easy to structure data replication jobs that automatically and continuously replicate Adobe Analytics data to any supported NoSQL database.
Can I export Adobe Analytics to S3?
Amazon Simple Storage Service (Amazon S3) is an object storage service providing scalability, data availability, security, and high performance. CData Sync offers the ability to create jobs that will transfer Adobe Analytics data to Amazon S3 to support backup, enable cost efficiencies, enforce security, and meet compliance requirements. CData Sync will convert Adobe Analytics data automatically into popular flat-file formats like CSV, parquet, and Avro and push those files to Amazon S3 as objects.