What is a data lake?
A data lake is a centralized storage solution that allows you to store large amounts of raw data, whether it is structured, semi-structured or unstructured. It is a large-scale platform designed for incorporating, managing and analyzing data from various sources. In this article, we will explore in detail what you need to know about data lakes, how they benefit you, how they work, and how they fit into the OVHcloud cloud ecosystem.
Why do I use a data lake?
With digital transformation, companies are generating a phenomenal amount of data from multiple sources, including online transactions, IoT sensors, social networks, and business applications. This explosion of data requires adapted storage solutions, as traditional systems quickly reach their limits in terms of flexibility and cost.
The data lake is characterized by its ability to store raw data without requiring any prior transformation. This allows companies to keep all their data, even if its immediate usefulness is not obvious, which is crucial in the era of big data . A data lake centralizes information from several sources and pushes back the time when this data needs to be organized.
Centralize and reduce data pools
A data lake centralizes data from various sources (CRM, ERP, social networks, IoT, etc.) in one place. This centralization reduces data pools, where each department of a company keeps its own data, without sharing it. Global access to data enables collaboration and more informed decision making.
A solution adapted to the cloud era
Modern data lakes, especially when integrated into cloud infrastructure, offer more flexibility and scalability while reducing the costs associated with physical infrastructure. By utilizing an OVHcloud cloud solution, companies can adapt their storage capacity to suit their evolving needs, without sacrificing data availability and security.
How does a data lake work?
A data lake is a flexible storage space that can absorb and store data in its raw state, without any prior transformation. Unlike traditional systems such as data warehouses (EDD), which need to be structured before they can be used, a data lake allows this step to be deferred. Here are the operating systems:
Data collection
A data lake collects data from various sources, such as databases, IoT sensors, log files, financial transactions and videos. It can be ingested in batches or in real time, depending on your needs. With its streaming capabilities, a data lake is able to capture data streams in real time. This feature is useful for non-loss-enabled applications, such as financial transaction tracking.
Data storage.
Once ingested, the data is stored in its original format. A data lake can handle all kinds of data (structured, semi-structured, etc.). One of its key advantages is its ability to store this data without pre-structuring, allowing you to focus on quickly or even real-time data ingestion.
A modern data lake often relies on cloud infrastructure that offer an almost unlimited scalable storage capacity. Cloud solutions such as those from OVHcloud thus allow for storage capacity to be increased as needs grow, without having to worry about infrastructure constraints.
Metadata management and inventory
As the amount of data grows, it becomes essential that it be managed efficiently. This is where metadata plays a crucial role. They describe the data and make it easier to find and use. Data inventory is essential for organizing information, making it easier for different areas of the business to use.
Metadata also helps ensure appropriate data governance, ensuring sensitive or critical information is managed securely and in compliance with various applicable regulations.
Data analysis
Once the data has been stored and indexed, it can be analyzed using a dedicated tool. A key advantage of a data lake is that it can process many types of data with different analytics technologies, such as descriptive, predictive, or machine learning analytics.
Data lakes are often combined with advanced analytics and visualization tools, allowing companies to generate meaningful insights from their data. Using a cloud analytics solution also makes it easier to process and analyze data on a large scale.
The advantages of a data lake
1. Scalable, low-cost storage
Let’s start with one of the key benefits of the data lake: its ability to store very large volumes of data at a lower cost. Unlike traditional databases, which become expensive as volumes grow, this solution is relatively scalable and cost-effective. Combined with cloud services, such as those from OVHcloud, it can help to reduce physical infrastructure spending while adjusting storage requirements based on demand.
2. Flexibility in data storage
Another major advantage of the data lake is the flexibility it offers for storing unstructured data. Companies increasingly produce data in the form of videos, images, text files, and social media interactions. Unlike relational databases, a data lake keeps this data in its raw state.
3. Centralized access to data
By centralizing all of the company’s data in one data lake, it becomes easier to access and use information for a variety of purposes. This centralization breaks down data reservoirs, fostering collaboration between a company’s different departments. This enables organizations to leverage data more efficiently to make better decisions.
4. A lever of innovation
Data lakes are real catalysts for innovation. By bringing all of the company’s data together, analysts and data scientists can experiment with new data analytics models, test machine learning algorithms, and implement AI projects. They can be used to develop predictive analytics, improve operations management and customize services.
5. Optimization for big data
A data lake is designed to meet the needs of big data projects, which require the processing of massive volumes of data. With their ability to store large amounts of data, they enable companies to maximize the use of big data technologies, such as real-time analytics or machine learning.
Data lake: some concrete examples
Financial services sector
Financial institutions use data lakes to centralize and leverage data from multiple sources, such as banking transactions, credit records, and customer behaviors. This allows them to better understand their habits, improve fraud detection, and create predictive models to better manage risks.
Health
In healthcare, a data lake can store and use very high volumes of medical data, such as patient records or linked imaging. This speeds up medical research and improves treatment through better customization. The data lakes also offer fast access to all this data, which is essential for research and improvement of medical practices.
E-commerce
E-commerce use data lakes to better understand customer behavior, based on data such as purchase history, tracking website browsing and social media interactions. This information is then used to customize product recommendations, optimize marketing campaigns, and improve customer experience.
IoT (Internet of Things)
The IoT devices generate a significant amount of real-time data. Data lakes are used to store this information and analyze it in a variety of contexts, such as predictive maintenance, infrastructure management, or production line optimization. They enable companies to better monitor and manage their connected devices, while optimizing their performance.
The data lake compared to the data lakehouse
The data lakehouse concept has emerged to address some of the limitations of traditional data lakes. A data lakehouse combines the benefits of data lakes and a data warehouse. Data lakehouses offer better governance and a more rigid organization of data, while retaining the ability to manage unstructured information.
A data lakehouse allows companies to take advantage of data lakes’ advanced analytical capabilities, while taking advantage of data management best practices.
This hybrid approach is particularly useful for companies looking to maximize flexibility while meeting data warehouse governance and performance requirements.
OVHcloud and the data lake
OVHcloud offers several solutions adapted to the needs of companies so that they can manage and analyze their data lakes. Here are three key products:
OVHcloud Object Storage
A scalable cloud storage solution, ideal for storing large amounts of heterogeneous data in data lakes. This solution enables easy access to large databases and efficient database management.
Data processing with OVHcloud
This service provides high processing power to analyze large data sets stored in your data lake, using the cloud infrastructure for machine learning and data analysis.
OVHcloud Big Data Platform
A comprehensive platform that provides solutions for managing big data. This enables companies to efficiently process, analyze and visualize data stored in data lakes.