Data lakes are larger data repositories than data warehouses, which provide the greatest ease and largest capacity for storing nearly any type of data format. Data lakes are often the first repository in a data stack, receiving the influx of all raw, semi-structured, and structured data that applications and infrastructure produce by a company, and acting as the organization's central data repository.
Because the speed and volume of data growth have only been accelerating in our digital world and are expected to accelerate more as IoT connects more devices than ever to the Internet, data lakes were created to solve the massive job of rapidly ingesting and storing a diverse collection of Big Data sets.
Functionally, data lakes operate by storing data differently than other repositories, foregoing the added step of data analysis that data warehouses perform. Because data lakes do not perform data analysis (not true in emerging cases, as newer technology is available that enables data lakes advanced analytics features), they do not bother with structuring data before storing, rather they simply store the data in its native format, speeding up ingestion.
Data lakes use flat architectures and Object Storage rather than hierarchical file systems found in data warehouses. Object Storage tags data with metatags and unique identifiers making it possible to easily retrieve data later. This is considered a schema-read principle, where data is stored with no pre-defined data schema. Data lakes can then be used by data warehouse analytics systems, dipping into the lake and pulling out the desired data that is then parsed and adapted to a data schema and moved to the data warehouse, analyzed, refined, and combined with other data sets.
Many enterprises gain significant business insights from their data which can be leveraged to get a foothold over their competitors. Faced with the increasing costs of collecting and processing Big Data sets, and to stay ahead, they turn to the advantages of data lakes: open-format, low-cost scaling, and advanced machine learning analytics.
Open-format allows the storage of any type of unstructured, semi-structured, and structured data, so, enterprises that struggle to maintain operations while uncovering data insights can simply dump all their data into their data lake and sort through it later because it's stored in its original form. Likewise, data scientists can return to the data lake at any time and like an archeological dig find undiscovered insights.
While data lakes can be on-premises, providing centralization and control, many enterprises are moving their data lakes to the cloud for superior flexibility and scalability. And because data is stored in raw formats, enterprises can avoid vendor lock-in, though switching vendors entails moving vast sets of data (petabytes and more) which can be time-consuming.
The raw data in data lakes can be held indefinitely, allowing data scientists to continuously transform it into actionable analytics. To help them sift through the waters, data lakes can be integrated with AI and machine learning solutions that apply analytics to these sets of unstructured and structured data. The ability for AI to analyze any and all types of data has become a future focus of enterprises.
Data lake benefits:
Data warehouses, unlike data lakes, are considered scheme-write systems, meaning that when data is stored in a data warehouse, it is fitted into a predefined data scheme which helps in cataloging and organizing. This process alludes to the fact that data warehouses are designed to carefully prepare data before storage so that analysis can quickly follow.
Though data warehouses cannot store the same volume as data lakes, to try would be exceptionally cost-prohibitive, they are helpful in processing immediate, critical data metrics helpful to real-time business operations. Oftentimes, enterprises use data lakes as a base in their data stack, connecting it to data warehouses, or other AI and machine learning analytics through their data pipeline.
Data lakes are broader data repository systems with data ingestion as a primary concern over data analysis. Though analytics is developing around data lakes, data lakes are highly inclusive, accepting all data types, supporting all users, and easy to adapt. Because of these characteristics, data lakes potentially hold the deepest business insights. The challenge in drawing out those insights is defined by the very data lake characteristics that enable deep insights, so much data and the breadth of diversity requires time to process and analyze.
In contrast, data warehouses standardize data formats at ingestion so that insights can be quickly delivered about domain-specific channels on time, such as marketing insights, or account billings. Conceptually, data warehouses represent an increase in data refinement at the sacrifice of data scope over data lakes.
Many of the top cloud vendors also offer leading data lake solutions. When choosing a data lake ask:
The top cloud data lake solutions in 2021 are:
Data lakes have the potential of becoming a fundamentally critical piece of many enterprises’ IT makeup. Despite the advantages that drive companies to use data lakes, they are still emerging as a technology and therefore have challenges yet to be overcome. Most of these challenges stem from the fact that data resides in a single morass of data types and sets that muddy reliability, performance, security, and data governance. This is referred to as a data swamp, and results from:
The journey to build your data lake could take anywhere from 3 months to implement basic functionality, and up to a year to implement it with advanced analytics and machine learning using a leading cloud provider like AWS. The following best practices can help prevent future challenges if applied during all phases of data lake design and operations.
Data is ubiquitous, and how we choose to use it makes it valuable or simply cluttered. The main use case of data lakes is to rapidly ingest and store real-time streaming data flow and batch processing data, in any format, and then secondarily perform analytics on sets of diverse data. To that end, large multinationals, manufacturing, municipalities, and other companies have leveraged data lakes for many businesses uses: