Seven data integration and quality scenarios for Qlik and Talend solutions

By Clive Bearman
rectangle with dark blue textured background displaying the Qlik and Talend logos with the phrase "What's next is now."rectangle with dark blue textured background displaying the Qlik and Talend logos with the phrase "What's next is now."

Exploring the art of the possible

By now, you should have read the headlines that Qlik's acquisition of Talend is complete, and we're excited to expand our best-in-class capabilities to help you access, transform, trust, analyze, and take action with your data. You might have seen Mike Capone's QlikWorld keynote or the recent "What's Next is Now" webinar and wondered how to leverage these new capabilities in your organization.

Flow chart showing how Qlik and Talend work together in three areas: data integration and quality, analytics, and foundational servicesFlow chart showing how Qlik and Talend work together in three areas: data integration and quality, analytics, and foundational services

There's a mind-boggling number of data integration and quality scenarios we could now embrace, but the list below describes seven everyday uses that crop up in most companies, regardless of size, industry vertical, or geography. Incidentally, the following list is not ordered by importance, market size, or strategic focus. Also, the scenarios are not mutually exclusive, and it's common for an organization to implement several use cases concurrently. So let's examine the seven data integration and quality uses for Qlik and Talend.

1. Database-to-database synchronization

Database-to-database synchronization is the mainstay use case for many of us at Qlik and Talend. The combined functionality offers you tremendous flexibility for whatever problem you're trying to solve. So whether you use basic data loading, real-time replication, or micro-batch updates, we've got you covered. In fact, database-to-database is most commonly used to solve the following issues:

  • Real-time data for reporting and analytics: Replicating data to a separate database or warehouse can allow for faster and more efficient querying and analysis of the data, without impacting the performance of the primary database.
  • Real-time data integration: Replicating data between databases can facilitate data integration between different systems or applications across the organization to ensure that data is consistent and up to date.
  • Legacy modernization: Offloading legacy data to new data stores to reduce OLAP costs and improve query performance.
  • Cloud data movement: Replicating data between on-premises data sources and cloud databases for new ML initiatives.

2. Data warehouse modernization

Our second use case focuses on data warehouse modernization, which refers to automating a cloud data warehouse's design, deployment, and operation. Data warehouse automation offers faster time to market for new data warehouses, improves data quality, and reduces costs associated with manual administration. Qlik's secret sauce is the intelligent data pipelines that help organizations scale their data warehousing efforts more efficiently by automatically generating the necessary transformation SQL and pushing it down to the warehouse for execution.

3. Data lake/lakehouse automation

No segment of the data integration market has seen so much change in recent years as the data lake. Consequently, there are many approaches to data lake implementation, and once again, our combined portfolio can support any architecture. Our data lake automation solutions help you move enterprise data, transform it, and enforce data governance policies to help you build a data lake for your data analytics, machine learning, and AI initiatives — regardless if your lake is based on Hadoop, cloud-object stores, or Databricks.

4. Database-to-streams/streams-to-database (or other destinations)

Integrating databases with streaming infrastructures like Apache Kafka or Amazon Kinesis can help organizations gain insights from their dynamic data and respond more quickly to changing business conditions. For example, a company might use a database to store customer data, such as their name, address, and purchase history. They could then use a streaming infrastructure like Kafka to process purchase data as it is generated in real time to highlight nefarious behavior such as fraudulent credit card transactions. Our data integration and quality solutions can synchronize database transactions with streams and also source data from streams to route to any destination in virtually any format.

5. Data quality and governance

Accurate data is the lifeblood of any successful initiative that drives organizational excellence. Consequently, data quality is crucial to any business process, especially:

  • Data analysis: Good-quality data is essential for accurate data analysis.
  • Customer relationship management: Accurate data helps businesses to better understand their customers and provide superior customer service.
  • Risk management: High-quality data helps businesses identify risks and take appropriate actions to mitigate them.
  • Marketing: Correct data helps businesses target their marketing efforts more effectively.
  • Financial reporting: Precise data helps businesses produce accurate financial reports.

6. API services and workflow

APIs are an intermediary layer that helps companies safely expose their application data and functionality to external third-party developers, business partners, and other company departments to encourage collaboration and drive innovation. Qlik and Talend's portfolio enable you to create and consume your own APIs for scenarios such as:

  • Driving collaboration: Create organizational APIs as part of a cloud-first strategy
  • Delivering innovation: Build new applications that leverage existing data and functionality via APIs.
  • Controlling access: Publish APIs that control data exchange between multiple parties
  • Adopting new architectures: Create "data contracts" as part of a data mesh
  • Enabling automation: Automate business processes such as order processing, inventory management, and customer support
  • Improving efficiency: Integrate different systems such as CRM, ERP, and e-commerce platforms
  • Implementing reverse ETL: Write back KPIs from data warehouse to operational systems

7. Operational data transformation

Our last category is operational data transformation. This converts raw data into formats that can be used by downstream processes such as electronic data exchange, data science, or analytics. Typically, operational data transformation occurs outside the data warehouse or lake, with the final files saved in an object store. An example: converting transactional records into HL7 files, transforming CSV files to Parquet, and converting aggregate data sources into EDI consumable formats. Our data integration and quality solutions contain specialized functionality for many common transformations and will help you rapidly solve the data exchange problem for specific industry formats.

I think you'll agree that the Qlik and Talend solutions are incredibly complementary and the expanded capabilities will help you solve more business problems throughout your business. Moreover, Qlik remains open to virtually any data source, target, architecture, or methodology to ensure your customers always have the data they want whenever needed. Finally, if one or more of these seven use cases need addressing at your company, don't hesitate to contact us. We'd love to show you the industry's most comprehensive, agile, enterprise-class data integration and quality solutions.

You can learn more about how the combined portfolio can unlock the power of your data in our webinar: The Art of the Possible: Qlik + Talend Win With Data.