A data lake captures both relational and non-relational data from a variety of sources—business applications, mobile apps, IoT devices, social media, or streaming—without having to define the structure or schema of the data until it is read.

A data lake is usually a single store of data including raw copies of source system data, sensor data, social data etc., [2] and transformed data used for tasks such as reporting, visualization, advanced analytics, and machine learning.

Data lakes allow you to store relational data like operational databases and data from line of business applications, and non-relational data like mobile apps, IoT devices, and social media. They also give you the ability to understand what data is in the lake through crawling, cataloging, and indexing of data.

2024年8月26日 · Data lake stores are optimized for scaling their size to terabytes and petabytes of data. The data typically comes from multiple diverse sources and can include structured, semi-structured, or unstructured data. A data lake helps you store everything in its original, untransformed state.

2024年2月2日 · A Data Lake serves as a centralized repository that can store massive volumes of raw data until it is needed for analysis. In this article, Let's delve into the key points that shed light on how Data Lakes efficiently manage, and store raw data for later use, Data Lake architecture, and the Challenges of Data Lakes.

Data lakes allow you to transform raw data into structured data that is ready for SQL analytics, data science and machine learning with low latency. Raw data can be retained indefinitely at low cost for future use in machine learning and analytics.

Data lakes use metadata and cataloging systems to index and organize data, making it discoverable and accessible for analytics. Data governance, security, and access controls are also essential components of a data lake, ensuring data quality, compliance, and protection against unauthorized access.

2025年1月16日 · A data lake is a low-cost data storage environment designed to handle massive amounts of raw data in any format, including structured, semi-structured and unstructured data. Most data lakes use cloud-based object storage, such as AWS S3, Google Cloud Storage or IBM Cloud® Object Storage.

2024年1月3日 · Data lakes and data warehouses are storage systems for big data used by data scientists, data engineers, and business analysts. Despite their similarities, though, they're more different than they are similar, and understanding these key differences is important for any aspiring data professional.

2025年2月6日 · A data lake is a centralized repository that stores vast amounts of structured, semi-structured, and unstructured data in its raw form. Unlike traditional databases or data warehouses, which require data to be pre-processed and structured before storage, data lakes allow businesses to ingest data first and structure it later based on their ...