Converging Data Architectures – Data Warehouses, Data Lakes or Lakehouse

We just crossed the one-year anniversary of COVID-19. When I look back, it is easy to see how the crisis not only managed to turn our lives upside down, but also acted as a shot in the arm for digital transformation. Telemedicine, remote banking, online shopping and virtual learning – all took off at a pace unimaginable just a year ago. In fact, research by McKinsey shows that businesses vaulted five years forward in their digital journey in just two months during the pandemic.

As data and technology leaders rush to update and modernize old, disconnected enterprise data architectures to move toward a unified analytics environment, they are recognizing that they no longer need to be limited by their existing data architectures: data warehouses for structured, aggregated views of data; data lakes for massive volumes of raw data.

The cloud has made the transition easier. Companies don’t have to make an either/or decision between a data warehouse and a data lake, or even to establish separate-but-equal entities in the cloud. Object storage in the cloud enables them to store all types of data – structured, unstructured or semi-structured – and provides the scalability they need to manage huge volumes of data. Separation of storage and compute allows for each to be scaled up or down independently, blurring the lines between traditional data warehouses and data lakes. The separation also enables companies to architect a multi-modal lakehouse platform, which provides a single source of truth for all analytic initiatives – AI, BI, machine learning, streaming analytics, data science and more.

As the data lake and data warehouse worlds collide, no matter which camp you fall in – data warehouse, data lake or lakehouse – one thing is clear. Digital transformation is only possible with a modern, automated approach to data integration. Traditional ways of manually configuring and managing data pipelines and ETL scripting are too slow, cumbersome and error-prone to productionize analytics pipelines in the time your data consumers need.

Qlik Data Integration automates the entire data warehouse and data lake pipeline, all the way from real-time data ingestion, to creation, cataloging and publishing of analytics-ready datasets. Further, we recognize that enterprise data architectures are in a state of flux, with organizations having one, both or converged data lake and data warehouse environments. Qlik Compose facilitates both data lake and data warehouse automation in one unified user interface, enabling you to plan and execute either project with ease.

Learn more about Qlik Data Integration and how we help companies like Aegon and EWALs Cargo Care accelerate their digital transformation.

As #Data & #Tech leaders move to unified analytics environment, they no longer need to be limited by their existing data architectures, per @Qlik's Ritu Jain

 

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