In today’s fast-paced digital landscape, managing the volume, variety, velocity, and veracity of data is essential. To stay competitive, organizations need a highly agile data management system. This is where the distinction between data lakes and data warehouses becomes crucial. In this second installment of our data lake series, we’ll explore the fundamental differences between these two storage solutions.

If you missed our first article on defining data lakes, be sure to check it out.

Both data lakes and data warehouses serve as central repositories for integrated data from various business intelligence and operational systems. While they might seem similar at first glance, they have significant differences when you look closer. Here are the key distinctions your organization should be aware of:

 

Structure

Data warehouses use a pre-defined, structured format for storing data, typically in relational tables with rows and columns. This structure is defined in a schema before any data is stored.

On the other hand, data lakes are more flexible. They can store unstructured data (like plain text and emails), binary data (such as images and videos), semi-structured data (like logs and JSON files), and structured data from relational databases. The schema in a data lake is defined at the time of data reading, not writing, allowing for greater flexibility in data storage.

 

Data Extraction

Data warehouses are designed for easy access by analysts and users with less technical expertise. Data lakes, however, require data professionals and scientists to extract value due to the vast amount of data, its varied formats, and the rapid evolution of information.

 

Velocity

Data lakes excel in the speed at which they can ingest data. Their distributed design allows them to scale out, enhancing ingestion speed. This capability supports use cases like real-time sentiment analysis from social media streams or quality control from manufacturing sensors.

 

Agility

Data warehouses, with their schema-on-write characteristic, are less agile in adapting to changing business needs. Any changes to the data structure require significant adjustments to the ingestion processes, ETL (extract, transform, load) pipelines, and APIs or visualizations.

In contrast, data lakes’ schema-on-read approach provides unmatched flexibility. Enterprises can store data in its native format and later repurpose it for different analyses without altering the core architecture. This adaptability supports ad-hoc analysis and data discovery, allowing data professionals to explore and derive insights driven by their train of thought.

 

Costs

While data warehouses can handle large amounts of data, their relational database model isn’t optimized for distributed hardware architectures. This can lead to performance and latency issues, making it expensive to scale up. Data lakes, however, benefit from open-source big data technologies that allow horizontal scaling with cost-effective hardware.

 

As data lakes become more prevalent, their security measures are also improving, often surpassing those of traditional data warehouses. Considering their agility and cost-effectiveness, data lakes are becoming the preferred storage option for many organizations.

In our next article, we’ll delve deeper into the value that data lakes bring to the table. Stay tuned!