How Does Data Integration Work?
From Data to Insights
The goal is to process the raw information from your database and convert it into smarter and more eco-friendly insights. Once the relevant data is accumulated from databases, apps, and files, then it is further narrowed down into a more unified function. This gives birth to the formulation of automated Data Warehousing, Managed Data Lake Creation, and Data Streaming.
Precision Based Analytics
Now since the data is all narrowed down to defined perimeters, the next step of Data Integration is to channel this data down in the form of precision-based analytics tools and data lakes.
Enterprise-wide monitoring and control
Data lakes basically make it easier for you to manage your data on-the-go, with managed infrastructural support of Amazon AWS, Microsoft, Azure, Google Cloud, and other cloud service providers. The data is utilized for three deliverables:
To transform raw data streams into cloud-based lakes
To refine your database for analytics tools
To manage a catalog and discover the potential of your data
Data Integration Services
Data Integration Services have also gained traction as industries continue to facilitate the availability of data, this ease of access to data is vastly facilitated by cloud-based systems. A few most prevalent modes of Data Integration services include Data Streaming, Data Warehousing Automation, and Managed Data Lake Creation.
Data Streaming
- Log-based change data capture
- Zero footprint architecture
- Cloud-optimized
- Free Trial
- Download Streaming Change Data Capture eBook
- Change Data Capture 101: What Works Best – and Why
Data Warehouse Automation
This solution particularly revolves around the induction of enterprise data and transferring it onto data warehouses on a universal scale. This process is continuously refined and automated to improve efficiency whether it’s at a physical location or over the cloud.
Qlik Replicate (formerly Attunity Replicate) supports the broadest range of sources and targets, enabling you to load, ingest, migrate, distribute,
consolidate and synchronize data on-premise and across cloud or
hybrid environments. These include:
RDBMS: Oracle, SQL, DB2, MySQL, Sybase, PostgreSQL
Data warehouses: Exadata, Teradata, IBM Netezza, Vertica, Pivotal, MS SQL Data Warehouse
Cloud: AWS, Azure, Google Cloud
Hadoop: Apache, Cloudera, Hortonworks, MapR
Streaming platforms: Apache Kafka, Confluent
Enterprise applications: SAP
Legacy: IMS/DB, DB2 z/OS, RMS, VSAM
Please refer to the support matrix for a complete list of connectivity options.
Data Lake
- Explore Qlik Enterprise Manager™ (formerly Attunity Enterprise Manager)
Learn more about our unique data streaming
capabilities for these leading platforms.
Deploy on Microsoft Azure and integrate with Microsoft applications.
Learn More
Deploy on Microsoft Azure and integrate with Microsoft applications.
Learn More
Deploy on Microsoft Azure and integrate with Microsoft applications.
Learn More
Deploy on Microsoft Azure and integrate with Microsoft applications.
Learn More
Deploy on Microsoft Azure and integrate with Microsoft applications.
Learn More
EBOOK
Data Warehouse Automation in Azure for Dummies
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DATASHEET
Leveraging Mainframe Data for Modern Analytics
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ON-DEMAND WEBINAR
Express Scripts Driving Digital Transformation from Mainframe to Microservices
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VIDEO
How Vanguard is migrating data to AWS with Qlik