Data Lifecycle Management

What is Data Lifecycle Management?

For organizations seeking to realize the benefits and value of Big Data, the right data lifecycle management technology can help to simplify ingestion, storage, processing, validation and security of business-critical information.

Working with Big Data is no picnic. IT teams tasked with Big Data lifecycle management must find solutions for effectively managing vast amounts of information that is growing at exponential rates. To enable easier manipulation of data, IT needs a Big Data solution for integrating, combining and reconciling disparate data sources. Selecting the right analytics tools for generating real-time insight is critical. And optimizing performance while reducing cost and reliance on developer resources must also be a priority.

For enterprises considering a data management company as a partner in Big Data lifecycle management, Qlik provides a portfolio of data management software that can help to accelerate data delivery and availability, automate data lifecycle management processes and optimize enterprise data management with intelligence.

Simplifying Data Lifecycle Management

Qlik is a pioneer in heterogeneous data availability, streamlining data lifecycle management for greater agility, reduced complexity and improved efficiency. By simplifying and automating Big Data management, Qlik helps companies worldwide capture more value from more data while saving time and cost.

Qlik software supports many styles integration across the industry's broadest array of platforms, with solutions that address the challenges of data lifecycle management on-premises or in the cloud, for data management structures that include data warehouses, modern databases, Hadoop, SAP and real-time messaging systems like Kafka.

Benefits for Data Lifecycle Management

Our solutions for Big Data lifecycle management address the many challenges of ingesting, storing, processing and analyzing diverse and large volumes of data. With Qlik data management services and solutions, organizations can:

• Accelerate data ingestion at scale from a wide variety of sources into a Hadoop data lake.
• Create streaming database feeds to Kafka to enable real-time analytics with live data from many sources.
• Integrate mainframe systems with Big Data technology by offloading queries from the mainframe and enabling analytics through Apache Kafka and Big Data environments, including all major Hadoop distributions.
• Optimize data warehouses with Hadoop by rebalancing data workloads for lower costs and better performance.
• Manage data lakes with greater intelligence and more sophisticated governance, using Qlik data usage and workload analytics to streamline data lifecycle management tasks and optimize capacity planning, lower costs and improve compliance.