Data Integration

Meet Growing Demands for Analytics-ready Data Sets with Six Data Integration Reference Architectures

Headshot of blog author Anand Rao

Anand Rao

5 min read

Photo of a person with outstretched hand tapping icons projected on a glass

All the data your organization collects should contribute to delivering real growth, innovation, and a competitive edge to your business. Yet as more data floods into your environment faster from more sources than ever, people-intensive integration tools are getting in the way.

They’re bottlenecking the process of delivering analytics-ready data to all your business initiatives the moment they’re needed, which is creating significant challenges when it comes to assessing data’s value and identifying valuable resources. That’s no way to get ahead.

In need of a better approach? Consider these important questions:

  • How will you improve the speed at which you deliver data?

  • Can you increase the volume of data you deliver with your current model?

  • How will you boost access to data you provide to your people and teams?

  • Can you grow the efficiency of your data integration process with existing tools?

The Need is Clear

Organizational leaders aren’t shy about expressing what they want from you, data engineers, and data integration solutions to accelerate top business initiatives:

  • Integrated access to all data – Leaders want to bring together increasingly high volumes of data from a growing array of sources and replicate it to data management and analytics platforms – without production app disruption

  • Governance – Business leaders expect IT to track, maintain, and protect data at every stage of the lifecycle

  • Agility – Leaders hope for the automation of the design, creation, and continuous updating of data warehouses and data lakes on any cloud platform to speed decision making

Six Data Integration Use Cases

Effectively balance rising demands for data – at speed – against security and performance risks with modern, secure, efficient data-to-analytics pipelines through DataOps for analytics from Qlik. Discover the value of Qlik Data Integration for bringing together all data from many sources and making it more valuable to your business in these detailed reference architectures for six popular use cases.

  1. Data Warehouse – Meet or exceed the demands for analytics-ready data marts that enable data-driven insights at the speed of change.

  2. Data Lake – Realize a faster return on data lake investments while confidently meeting growing demands for analytics-ready data sets in real-time.

  3. Data Lakehouse – Unify both data lake and data warehouse automation in one user interface to plan and execute either with ease.

  4. Event-Driven Data – Lambda – Reliably update a data lake and efficiently train ML models using three layers to predict upcoming events accurately.

  5. Event-Driven Data – Kappa – Invest in less expensive hardware and solve multi-layered, Lambda architecture redundancy by replaying data instead of maintaining two code bases.

  6. Data Mesh – Derive value from analytical data at scale while the data landscape, use cases, and responses constantly change.

Accelerate Your Business with Qlik

Qlik Data Integration automates the creation of data streams from core transactional and enterprise systems, efficiently moving data to applications, data warehouses, and data lakes in the cloud or on-premises, and then cataloging and delivering analytics-ready data to Qlik Sense or other analytics solutions including those provided by the major cloud providers.

By quickly delivering data to the user without typical business friction, Qlik Data Integration powers the agility necessary to drive needed business value out of scattered and disparate data.

Visit Qlik Data Integration to learn more.

All the data your organization collects should contribute to delivering real growth, innovation, and a competitive edge to your business.

Ready To Get Started?