Data lakes — what they are, when they’re used, and more
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.
Here are some common scenarios when a data lake might be necessary:
- Big data processing. If you have large volumes of data that need to be processed and analyzed, a data lake can provide a scalable and cost-effective solution.
- Unstructured data. If your organization works with unstructured data, such as video, audio, images, and text files, a data lake can be an ideal solution. The data can be stored in its raw form, allowing you to run various analytics and artificial intelligence (AI) models to extract insights.
- Real-time data processing. If you need to process data in real time or near-real time, a data lake can help you capture and process data quickly. Data lakes can be useful for building real-time analytics dashboards.
- Cost-effective storage. Data lakes can be a cost-effective way to store large volumes of data. Since data is stored in its raw form, you don’t need to spend time and resources structuring or cleaning the data before storing it.
- Collaboration. Data lakes can be used to centralize data from various departments within an organization, making it easier for teams to collaborate and share data. Data lakes can also be accessed by various stakeholders, including data analysts, data scientists, and business users, allowing them to perform their analysis and make data-driven decisions.
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.
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:
- Data quality. Since data lakes store raw unstructured and structured data, the quality of the data can vary. Without proper data quality controls, data lakes can become cluttered with low-quality or irrelevant data that is difficult to manage and analyze.
- Data governance. Since data lakes are designed to store large volumes of data, it can be challenging to ensure proper data governance. This can lead to issues such as data breaches, compliance violations, and data privacy concerns.
- Data security. Data lakes can be a potential target for cyberattacks. Since they store large amounts of sensitive data, security measures must be put in place to protect against data breaches and unauthorized access.
- Data silos. Data lakes can create data silos where data is not easily accessible to users across the organization. This can lead to inefficiencies and redundancies as users may end up storing their own copies of data — leading to inconsistencies and errors.
- Data integration. Integrating data from different sources into a data lake can be challenging, particularly when dealing with structured and unstructured data. Without proper integration, data may be poorly organized, leading to difficulties in searching, querying, and analyzing the data.
- Data management. Managing the data stored in a data lake can be challenging, particularly as the volume of data grows over time. It requires effective data management strategies to ensure that the data is properly organized, labeled, and tagged to make it easy to search, retrieve, and analyze.
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.