Unpacking the Data Lakehouse: A New Paradigm in Industrial Analytics

Fitting the round peg into the square hole isn’t always a perfect match – except maybe in sports. However, sometimes it is indeed possible to bring together two approaches that have long been considered independent: The Data Warehouse and the Data Lake can be combined into the Data Lakehouse. What’s the concept behind this evolution? And what are the real benefits?


From Data to Lakehouse

Companies are searching for solutions to reduce the ever-growing complexity of data processing while harnessing advanced analytics and machine learning capabilities, all without being constrained by existing data silos. Databricks, founded by the team behind Apache Spark, have crafted a smart Lakehouse platform that bridges the gap between traditional Data Lake and Data Warehouse concepts. The Data Lakehouse concept simplifies and expands how companies can use their data for business decisions, optimizations, and product development. Customers worldwide, such as H&M and Siemens, leverage Databricks Lakehouse services to control or rethink their business processes. At Device Insight, we specialize in IoT and Industrial Data Analytics. Seeing the potential of the Data Lakehouse Architecture for IoT use cases, we’ve become a certified Databricks Partner. Join us on the data journey! With this new blog series, we invite you to take a closer look at the opportunities and challenges of Industrial Data Analytics.

What is the idea behind a Data Lakehouse?

Now, let’s dive into the world of data analysis and processing. We will focus on the benefits and added value that the relatively new Lakehouse approach offers to various industries, such as manufacturing. In our first blog article, we address the fundamental question: What’s behind the buzzword “Data Lakehouse”? We explore the characteristics and real-world advantages of this relatively new innovative concept for data storage and analysis, ranging from improved data quality to accelerated analysis speed.

A Data Lakehouse is an advanced data architecture that combines the best features of Data Lakes and Data Warehouses. The idea is to merge the flexibility, scalability, and cost-efficiency of a Data Lake with the powerful analytical capabilities, governance, and structured querying of a Data Warehouse. The Lakehouse enables the storage of both structured data, such as classic database tables optimized for clear queries, and unstructured data. The latter can originate from various sources, including usage data from connected products, sensor, and telemetry data, as well as images from products and manufacturing processes. In essence, the Lakehouse concept creates a coherent platform that handles the diversity of data types while providing a robust foundation for comprehensive queries, analyses, and data processing.

The Databricks Data Lakehouse: A platform with enhanced benefits

Databricks’ approach to a Data Lakehouse goes even further, offering an integrated platform that combines a variety of data processing, data engineering, machine learning, and artificial intelligence features in a central, simple, and user-friendly environment. The main benefits are:

  1. Performance optimization: Thanks to Apache Spark, Databricks provides scalable and powerful Big Data processing. The Delta Engine accelerates queries and improves overall performance.
  2. Real-time processing: The platform supports Real Time Data Processing to provide up-to-date insights so you can respond even faster to changing conditions.
  3. Data Governance: Databricks enables effective data management with features for data quality, access controls, auditing, and data lineage. For example, it is possible to hide personal data for certain user groups, allowing groups with different permissions to work on the same Lakehouse.
  4. Collaboration and notebooks: The platform fosters team collaboration through collaborative workspaces and hardware that enable shared data analyses.
  5. Integrated ML and AI: It simplifies the integration of machine learning and artificial intelligence into data analysis and use cases.
  6. Flexible schema: With Delta Lake, companies can continuously adapt and evolve the database schema, for example, enriching it with additional information.

Securing flexibility for next-gen use cases and digital products

The Lakehouse architecture combines flexibility and analytical power, enabling companies to gain comprehensive insights from large datasets, efficiently manage data, and make data-driven decisions faster. Thanks to the incorporation of Cloud Analytics, it also becomes possible to analyze and visualize text data from various sources, making it easier to identify keywords and trends. Furthermore, the Databricks service is usable independently of cloud providers such as Microsoft Azure, Amazon Web Services, and Google Cloud, supporting a multi-cloud strategy.

In a nutshell, a Lakehouse can serve as a flexible foundation for data-driven business models that also incorporate machine learning and artificial intelligence. For example, retailers can combine sales data with social media feedback to create more targeted marketing campaigns and boost revenue. Manufacturing companies, on the other hand, use the Lakehouse approach to analyze sensor-based data in real-time, efficiently control production processes, and develop the next generation of digital products.

Blog Series Part 2: What's up next?

Stay tuned! In our next Data Analytics blog post, we will have a closer look at the upcoming regulatory developments that influence data handling. New European legislations like the EU Data Act, Data AI Act, and Cyber Resilience Act significantly impact the use and security of IoT data and should be considered early on.

+++ We’re IoT & Industrial Data experts. At Device Insight, we can actively implement the advantages of the Lakehouse concept for our customers. We integrate machine data with Databricks services to build use cases in advanced analytics and machine learning. +++