Qlik offers different facilities which streamline processes surrounding data movement: Qlik Replicate and Qlik Compose. In this post, we talk through benefits of these solutions…
Qlik Replicate is a tool which enables businesses to improve the efficiency of processes such as data ingestion, replication, and streaming between a wide variety of heterogeneous databases, data lakes and data warehouse platforms. Its agentless software moves your data quickly and securely, without impacting the performance of production data systems. With no need for manual coding, Qlik Replicate’s intuitive graphical user interface (GUI) makes life easier for administrators and enterprise architects by allowing them to quickly configure, control and monitor data replication.
Qlik Replicate’s high-performance change data capture (CDC) technology remotely scans transaction logs and rapidly delivers real-time data updates. In addition, any changes with data and metadata can be streamed across thousands of systems, such as any major RDBMS, legacy system, data warehouse, data lake or streaming platform. This allows you to maintain true real-time analytics with minimal overhead.
Qlik Compose can ease your workload by simplifying all facets of data lake and data warehouse design, development, data loading, deployment and updates, automating time-consuming, repetitive tasks that you would previously have done manually.
Qlik Compose for Data Lakes creates analytics-ready data sets by automating data lake pipelines, improving time-efficiency of data lake investments for businesses. It standardises and combines change streams into a single transformation-ready data store in the lake, automatically merging multi-table and/or multi-sourced data into a flexible format while retaining full history. This history gives you rapid access to trusted data, without the need to understand the automated structuring that has occurred.
Qlik Compose for Data Warehouses automates the design, deployment, and operation of agile data warehouses, improving time-efficiency of data warehouse projects by reducing the time and resources necessary to implement a data warehouse, data mart, or data hub. It eliminates mundane, error-prone data warehouse tasks that you would normally have to do yourself, by automating data modelling, ETL generation and production workflow. The time and cost of analytics projects based on cloud platforms, such as Amazon Redshift, Azure SQL Data Warehouse and Snowflake, is also reduced. Users can dynamically adjust data sources and models in response to changing business requirements to quickly load and iterate their data warehouses.
If you’d like to find out more about these solutions, get in touch with the Ometis team today.
Topic: Data integration