Data lakes are large-scale centralized repositories for raw data that enable organizations to store all forms of structured, semi-structured, and unstructured information in its original form.
Businesses can utilize this solution to store and access data across a range of use cases and applications, while performing data processing and analytics with greater speed and performance.
What is a data lake?
A data lake is a central storage system designed to hold all types of information. It can accommodate diverse data types ranging from structured (like databases) and semi-structured files to unstructured real time data, images or videos – this makes the data lake invaluable to modern applications that rely on quick and efficient access to various forms of information.
As digital space expands, so too does its need for flexible storage solutions – like data lakes – which meet fast data processing demands. In response to this need, companies have introduced solutions like cloud backup as one method of meeting this challenge.
Contrasting traditional data warehouses, which can be costly and proprietary solutions, data lakes enable organizations to store and access massive amounts of data cost effectively through one affordable solution that can be deployed on premises, in the cloud or a hybrid of both environments.
Data lakes differ from traditional data warehouses by not forcing data into an initial, predefined structure or schema prior to being stored – known as “schema-on-read.” This allows businesses to save both time and cost associated with creating these upfront structures, transformations, and schema.
Instead, they allow businesses to gather all types of data from multiple sources in its original format, enabling them to scale their systems according to changing business needs as they arise and save on storage and maintenance costs.
An advantage of data lakes lies in their ability to serve as an easily accessible hub of company’s most essential data. This feature is especially advantageous in an age when organizations must integrate information from multiple sources for a holistic picture of customers and operations.
Data lakes make it simpler for businesses to integrate machine learning and artificial intelligence tools, providing a fuller picture of their business, helping to make faster decisions with greater effectiveness.
Data lakes can accommodate raw, unprocessed data from various sources in its native format – ERP/CRM systems, IoT devices, social media platforms or even legacy systems – meaning it can be accessed from any application and analyzed for various purposes.
Dependent upon the size and nature of the data being stored, data lakes may be implemented using clusters of inexpensive yet scalable commodity hardware either on-premises or in the cloud. This approach significantly lowers both storage costs as well as preparation time required to get it ready for analytics use.
As well as helping reduce costs, data lakes can also help conceal or anonymize sensitive information for compliance with privacy policies and other security issues. They also serve to create intermediate data tables that facilitate faster processing and refinement of raw data sets.
Data lakes offer organizations a solution to many of the challenges arising in an ever-evolving digital landscape, including quickly collecting, organizing, and analyzing large amounts of data in an efficient manner so as to quickly identify areas of opportunity in their marketplace.
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Data lakes are repositories for unstructured data that can be easily accessed without first needing to define a data model. They can store all kinds of different forms of information – text documents, images, video streams and streaming data are among their capabilities.
A data lake is an essential element of big data solutions for many industries and can be leveraged in numerous ways to increase business intelligence. This includes integrating new sources of data with existing systems, ingestion speed increases and using self-service to enable users to easily access data needed for analysis.
One of the greatest challenges businesses are currently facing is managing an ever-increasing volume and variety of data. According to IDC projections, 80% of global unstructured data is expected to exist by 2025 – forcing organizations to make tough choices regarding how they approach this information.
Data warehouses are typically utilized by organizations for data storage purposes, providing reliable and trustworthy information for business use. While these systems offer great benefits in terms of reliable information retrieval, they do have limitations – for instance requiring data to be filtered or transformed before it can be stored within them.
Data warehouses often require extensive processing power in order to extract, transform and load (ETL) the data into their systems – this process may take an inordinately long time and prove costly for companies that need to store large volumes of information.
Data lakes offer companies that store large volumes of information a flexible and scalable storage option for managing this data, with more open formats being accessible for business intelligence use cases like machine learning.
Data lakes also boast the advantage of being highly scalable, meaning they can handle an increasing volume of data over time while giving organizations a competitive advantage by quickly adapting to market changes.
Data lakes provide an effective means of tracking the lineage of your data, making them useful in resolving issues such as an imbalance between historical and current data sets, or when working with multiple users across an organization who each have access to different parts of a lake.
Data lakes allow users to easily view data in various formats and can easily pull reports and insights on an ad hoc basis for analysis and decision-making purposes. With such flexibility available to them, more members of an organization have access to relevant information necessary for making better business decisions.
Data Lakes can be processed in various ways to enhance performance, including de-duplication, normalization and imputation to increase data processing performance. ETL jobs can then use more of this processed data for business analytics. Furthermore, data lakes can also be used to clean and index external integrations before training statistical models for classification, clustering and detection in machine learning pipelines.