Data lakes — what they are, when they’re used, and more

What is a data lake?

In this post, you’ll learn:

What is a data lake?

A data lake is a centralized data repository that allows for the storage of large volumes of structured, semi-structured, and unstructured data — in its native format, at any scale. The purpose of a data lake is to hold raw data in its original form, without the need for a predefined schema or structure. This means that data can be ingested from a wide variety of sources and stored in a more flexible and cost-effective way.

Data lakes typically use a distributed file system to store and manage data. This allows for the processing and analysis of large volumes of data in parallel using tools such as Apache Spark or Hive. Data lakes can also be integrated with a variety of data processing and analytics tools — including machine learning and artificial intelligence frameworks — to derive insights from the data.

When do you need a data lake?

One of the key benefits of a data lake is that it allows organizations to store and analyze data from a variety of sources, including social media, sensors, IoT devices, and other unstructured data sources. This can uncover new insights and use cases that weren’t previously possible with traditional data warehousing approaches.

Common data lake use cases

Here are some common scenarios when a data lake might be necessary:

Challenges with data lakes

It’s important to note that data lakes can also present challenges around data governance, security, and data quality, which must be addressed to ensure the data is accurate and trustworthy.

Challenges of data lakes

While data lakes offer many benefits — including flexibility and scalability — they can also present some problems. Here are some common challenges associated with data lakes:

So while data lakes are a valuable tool for storing and processing large amounts of raw data, these limitations may mean an alternative data management system would be better for your organization’s specific needs.

Using data lakes with Adobe Experience Platform

If you have a large volume of data that is coming from various sources and need flexible, cost-effective storage, advanced analytics, and data exploration, then a data lake might be the right solution for your organization.

Adobe Experience Platform helps centralize and standardize customer data and content across enterprises. With the Experience Platform API extensibility, you can integrate all your data sources into a single platform to drive personalized experiences at scale — all with a connected data foundation.

Watch an overview video or request a product demo to learn how Experience Platform can help you improve and extend your data management capabilities.