Qlik & Databricks: Join Forces to Accelerate Your Speed to Insights

Partnership Envisions a Data Lakehouse, an Architecture Designed to Merge the Best of Data Warehousing and Data Lakes

As the pace of business has accelerated, so has the need for speed and accuracy of insights. To keep up, most organizations have invested in a couple of key data architectures over the years: data warehouses, to store huge volumes of structured, relational data in a standard format and act as organizations’ system of records for business intelligence (BI) and reporting initiatives; and data lakes, to capture and store massive streams of raw data – structured, semi-structured, and unstructured – at low costs, and to power machine learning (ML) and data science initiatives. While both architectural constructs have been critical to enterprise data management, each comes with its own strengths and limitations.

As a result, we are starting to see data warehouse and data lake platforms blend their capabilities with the objective of providing a more unified platform. In a recent company blog post, Databricks laid out their vision for data lakehouse – which brings together the best of data warehousing and data lake approaches to provide a single source of truth for all analytic initiatives, including BI, Streaming Analytics, ML and Data Science.

Irrespective of the data architecture approach you favor or have invested in, if you are like most other organizations, you are still struggling to consolidate data from a multitude of heterogenous systems and sources into your data lake or warehouse, and are nowhere close to deriving real-time, predictive insights. Ingesting data from multiple sources, many of which are legacy systems and applications, and transforming it into a continuous stream of analytics-ready initiatives is not easy. Traditional approaches of scheduling daily updates and manual design and transformations are outdated. Slow and coding-intensive, these approaches most often result in error-prone data pipelines, data integrity and trust issues, and ultimately delayed time to insights. The key is to move to a modern, automated, real-time approach.

Databricks and Qlik: Fast-track Data Lake and Lakehouse ROI by Fully Automating Data Pipelines

Databricks and Qlik recognize the criticality of modernizing data pipelines for realizing the full potential from data lakes/lakehouse and analytic initiatives. To facilitate this modernization, the companies have joined forces to deliver a winning, real-time data pipeline automation solution for customers. By leveraging the Qlik Data Integration Platform (QDI) for fully automating data ingestion and transformation tasks and harnessing the power of Databricks and Delta Lake for ACID compliance, data quality and more, the joint solution accelerates the delivery of trusted, analytics-ready data, directly into the Databricks Unified Analytics Platform for faster predictive and actionable insights.

The joint solution provides customers:

  • Universal, Real-Time Data Ingestion. Industry-leading change data capture technology enables real-time data ingestion for massive volumes of transactional data from virtually all industry standard data sources — databases, data warehouses, legacy mainframe systems, SAP and more.
  • Flexibility & Agility to Adapt to Any Data Lake Structure Changes. The solution continuously captures and applies incremental changes in both data and metadata at near-zero latency.
  • Enterprise-Wide Monitoring & Control. DBAs and data engineers can configure, control and monitor thousands of streaming data pipelines across the enterprise through a single console, without any manual coding, for fast and easy setup and supervision.
  • Data Lake Pipeline Automation. The solution fully automates data pipelines – from data ingestion, to transformation, to creation of analytics-ready data sets, minimizing manual, code-intensive and error-prone design, ETL coding and scripting processes for improved productivity.
  • Up-to-Date, Consumption-Ready Data. Qlik auto-generates Spark transformations, making them ready to be pushed into the Spark engine for processing, so data scientists can focus on high-value modeling tasks and not waste time on data prep.
  • Governed, Trustworthy Data. Qlik persists history through “raw-to-ready” preparation process for end-to-end data lineage, as well as provisions full support for Delta Lake ACID capabilities to ensure transactional consistency, so data scientists can build/train ML models with confidence.
  • Integrated, Self-Service, Data Catalog. The solution gives data consumers visibility into all enterprise data through an integrated catalog — not limited to just what is in Databricks or Delta Lake — but across all enterprise sources. Better yet, the catalog provides data marketplaces, where data consumers can quickly search, preview and select and self-provision data.
  • Centralized Access Controls & Security. The solution allows administration of data access controls through a centralized platform. Built-in data obfuscation and encryption capabilities help ensure data is protected and secure to help with consumer data privacy regulations like GDPR and CCPA.

What are you waiting for? With Databricks and Qlik, you can now fast-track return from your data lake investments with more accurate, real-time, actionable insights.

Learn more about the joint offering or try for yourself. Take the solution for a test drive!

@Qlik joins w/ @Databricks in accelerating the creation of data lakehouse, an architecture designed to merge the best of #datawarehousing & #datalakes. Read Qlik's own Ritu Jain's latest blog post to learn more.

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