Even though data lakes and data warehouses are commonly used to store large amounts of information, the terms are not precisely equivalent nor interchangeable.
On the one hand, a data lake is a massive pool of raw data with no defined purpose. On the other hand, a data warehouse is a space where structured or processed data — that has been previously processed for a specified purpose — can be stored.
Thus, a data lake may be ideal for one organization, whereas a data warehouse may be more appropriate for another. These two types of data storage are sometimes misconstrued, yet they are fundamentally different.
This article guide will discuss:
- What are data lakes and who needs them
- The industry standard data lake solutions
- What are data warehouses and how they work
- The difference between data lake and data warehouse
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What is a Data Lake?
A Data Lake is a large-scale storage resource for raw, semi-structured, and unstructured data. It’s a location where you may save any sort of data in its original format, with no restrictions on account size or file size. It provides a large amount of data to improve native integration and analytic efficiency.
A data lake utilizes a simple framework to store data, whereas a hierarchical data warehouse typically stores data in files or folders. A unique identifier is generated for each data object in a lake, and it is labeled with a collection of enriched metadata tags. In the emergence of a business query, the data lake may be accessed to find relevant information, which can then be examined to help answer the query.
The goal of a data lake is to keep everything in its natural, unaltered condition. This is in contrast to a typical data warehouse, which changes and processes data as it is ingested.
Why is it called a Data Lake?
James Dixon, the CTO of Pentaho, is widely credited with coining the term “data lake.” He compares a data mart (a subset of a data warehouse) to a bottle of water that has been “cleansed, packaged and structured for easy consumption,” but a data lake is more comparable to a natural body of water. The lake receives data from the streams (source systems). The lake is open to the public for inspection or sampling.
He claimed that data marts had numerous intrinsic issues, such as information siloing. According to PwC, data lakes may “put an end to data silos.”
According to their data lake research, businesses were “starting to extract and store data for analytics into a single, Hadoop-based repository”. Data lakes are currently available in the following:
- Impetus Technologies
Who needs Data Lake?
Data scientists, data engineers, business analysts, executives, and product managers can highly benefit from a data lake. Not to mention, the prime objective of a data lake is to make organizational data from diverse sources become accessible to multiple end-users.
This is why the aforementioned professionals can benefit and even leverage insights in a cost-effective way to level up business efficiency. In addition, many forms of comprehensive analytics can only be done through data lakes.
Senior Staff Software Architect, The Walt Disney Company Caleb Jones said, “Domain-driven data platform aligns data and product source or target experts. It aligns with architectural and product evolution. The data lake also decouples domains so they can evolve independently, creates teams that are more focused, specialized, and have expertise around the domains and also gives product domains greater autonomy in their backlogs.”
By 2025, the worldwide data lake market is expected to be worth USD 24,308.0 million, increasing at a CAGR of 21.7%. In some organizations, data lakes have replaced data warehousing as a cost-effective option. Data warehousing, like data lakes, needs extra computer processing before reaching the warehouse. Managing a data lake is cheaper than that of a data warehouse due to the number of operations and resources needed to build the database for warehouses, which is boosting the global data lake market.
What are the components of a Data Lake architecture?
There are five key components of a data lake architecture. These components play a crucial role in understanding how a data lake works. To fully comprehend these components, let us refer to the table below from OpenMind.
Data Ingestion – The transfer of data from various sources to a storage medium where it can be accessed, utilized, and analyzed by an organization is known as data ingestion.
Data Storage – Data storage is defined as a magnetic, optical, or mechanical medium that stores and retains digital data for current and future actions.
Data Security – Data security is the process of safeguarding digital data throughout its lifespan from unwanted access, manipulation, or theft.
Data Lineage or Analysis – Understanding, documenting, and displaying data as it travels from data sources to consumers is known as data lineage. This contains all of the data’s changes along the route, including how the data was converted, what changed, and why.
Data Governance – The process of controlling the availability, accessibility, quality, and security of data in business systems, based on internal data standards and regulations that also manage data consumption, is known as data governance. Effective data governance guarantees that data is consistent, reliable, and secure and that it is not mishandled.
What are the best Data Lake solutions?
This list was created by the editors of Solutions Review to aid users in their quest for the finest cloud data lake solutions to meet their demands. Choosing the proper vendor and solution may be a difficult task that involves extensive study and consideration of factors other than the system’s technical capabilities.
The platform used is Google Data Lake. Any analysis on any type of data can be powered by Google Cloud’s data lake. This enables your teams to consume, store, and analyze massive amounts of varied, complete data in a safe and cost-effective manner.
Amazon Web Services
AWS data lake provides a solution that configures the basic AWS services required to quickly tag, search, share, convert, analyze, and control particular subsets of data across an organization or with external users.
Microsoft Azure Data Lake has all of the features that developers, data scientists, and analysts need to store data of any size, shape, or speed, as well as perform all sorts of processing and analytics across platforms and languages. Azure data lake also connects to operational stores and data warehouses, allowing you to extend existing data solutions or applications.
Snowflake is an Amazon Web Services-based cloud data repository. Data may be loaded and optimized from nearly any structured or unstructured input, including JSON, Avro, and XML. As a result of Snowflake’s extensive support for conventional SQL, users may perform modifications, deletes, diagnostic functions, transactions, and complicated connections. The tool does not require any administration or infrastructure. To compress data, produce reports, and conduct analytics, the columnar database engine employs sophisticated optimizations.
What is a Data Warehouse?
A data warehouse is a system that collects and organizes enormous volumes of data from many sources. Its analytical nature enables businesses to gain important business insights from their data, allowing them to make better decisions. It collects and stores historical records that may be extremely useful to data scientists and business analysts in the future.
Another definition describes a data warehouse as a centralized repository of data that can be examined to help people make better decisions. Data flows into a data warehouse on a regular basis from transaction processing systems, relational databases, and other sources.
Project managers, data engineers, business analysts, data scientists, and decision-makers use business intelligence tools, SQL clients, and other analytics software to access the data.
When IBM researchers Paul Murphy and Barry Devlin invented the commercial data warehouse in the 1980s, the notion of data warehouses became popular. Due to his writing of many books, including the Corporate Information Factory and other subjects on the creation, operation, and management of data warehouses, American computer scientist Bill Inmon was later considered and known as the “father” of the data warehouse.
How does a Data Warehouse work?
A data warehouse acts as key storage for data collected from a variety of sources. Data might be organized, semi-structured, or unstructured. The data is ingested, converted, and analyzed in the data warehouse before being made available to users for decision-making.
An organization may develop a more comprehensive analysis by combining huge amounts of data in a data warehouse, ensuring that it has examined all necessary details before reaching a conclusion.
In connection, a data warehouse architecture is a term that describes the general architecture of data transfer, processing, and display for end-user computing inside an organization. Each data warehouse is unique, yet they all have the same critical elements.
What are the Types of Data Warehouse?
There are 3 main types of Data Warehouses. Each of these types plays a significant role when it comes to providing support to various businesses and professionals.
Enterprise Data Warehouse
A centralized warehouse is an Enterprise Data Warehouse (EDW). It offers decision-making assistance to the entire organization. It provides a standardized framework for data organization and representation. It also has the capability of classifying data by subject and granting access based on such classifications.
Operational Data Store
Operational Data Store, or ODS, are simply data stores that are necessary when neither a data warehouse nor an OLTP system can meet a firm’s compliance requirements. The data warehouse in ODS is updated in real-time. As a result, it is commonly used for regular tasks such as maintaining corporate data.
The data warehouse is subdivided into data marts. It is tailored to a certain business segment, such as marketing, accounting, sales, or finance. Data may be collected straight from sources in an independent data mart.
Is the Data Warehouse still relevant today?
According to Valuates Reports, “The global data warehousing market size was valued at USD 21.18 Billion in 2019, and is projected to reach USD 51.18 Billion by 2028, growing at a CAGR of 10.7% from 2020 to 2028.”
When it comes to the impact of the COVID-19 pandemic on the data warehousing market, Valuates Reports revealed the following data:
“The data warehousing market has witnessed significant growth in the past few years; however, due to the outbreak of the COVID-19 pandemic, the market witnessed a sudden downfall in 2020. This is attributed to the implementation of lockdown by governments in the majority of the countries and the shutdown of travel across the world to prevent the transmission of the virus. The data warehousing market is projected to prosper in the upcoming years after the recovery from the COVID-19 pandemic. Various organizations across the globe have initiated work-from-home culture for their employees, which is creating demand for the cloud-based data warehousing software to manage and analyze critical information of organizations, thus, creating lucrative growth opportunities for the market.”
What are the Benefits of Data Warehouse?
Data warehouses can be very beneficial for your business. In fact, the data warehouse industry is expected to expand to $34 billion from its present size of $21 billion in the next five years.
Boosts Business Efficiency
Gathering data from many sources takes a lot of time for a business analyst or a data scientist. It’s considerably more convenient to have all of this information in one location, which is why a data warehouse is so useful. A data warehouse makes this information easily available — in the proper format – boosting the overall efficiency of the business operation.
Aids Business Intelligence and Analytics
High-quality, consistent data is required for business intelligence and analytics, and it must be delivered on time and be available for data mining quickly. This strength and speed are enabled by a data warehouse, which provides a significant advantage in critical business areas.
Ensures Quality of Data
Businesses generate data in a variety of formats. A data warehouse transforms this information into the formats that your analytics tools need. Furthermore, a data warehouse guarantees that data supplied by multiple business segments are of the same level of quality.
Access to Historical Data
From stock and production data to staff and intellectual property data, no organization can thrive without a big and reliable database of historical data. A data warehouse can give extensive historical data to a corporate executive who wants to know the sales of a major product a year ago.
What are the Most Popular Data Warehouse Tools?
Businesses may use a variety of data warehousing technologies to upload and analyze their data. Data warehouse software and data warehouse concepts are also available to learn more about data warehousing. When it comes to resources, the following are some of the most popular data warehouse tools:
- Amazon Redshift
- Amazon RDS
- Amazon S3
- Microsoft Azure
- Google BigQuery
- Micro Focus Vertica
- BI360 Data Warehouse
- Amazon DynamoDB
- SAP HANA
- Db2 Warehouse
How are they different: Data Lake vs. Data Warehouse Comparison
When trying to know the difference between a data lake and a data warehouse, it is important to keep in mind that a data lake is not a direct replacement for a data warehouse. Therefore, they’re supplementary technologies that cater to a variety of utilization cases, some of which overlap. And as mentioned earlier, the majority of companies that have a data lake also have a data warehouse.
Here are some of the key characteristics of a Data Lake:
- A data lake can hold vast volumes of structured, semi-structured, and unstructured data of various sorts.
- A data lake delivers huge data capabilities, such as the massive storage space and scalability required for large-scale data processing.
- A data lake offers enough storage to hold all of an organization’s data.
- A data lake includes stream computing, dynamic analytics, batch processing, and machine learning features, as well as task scheduling and administration capabilities.
- A data lake aids in the management of the whole data lifespan. A data lake holds the intermediate outcomes of analytics and processing, as well as comprehensive recordings of these operations, in addition to raw data. This allows you to track a data record’s full development process.
- A data lake provides substantial data retrieval and distribution capabilities. A data lake may accommodate a wide range of information sources. It collects complete and progressive data from data sources and saves it in a standard format. A data lake delivers the outputs of data analytics and computation to storage engines that may be accessed by many applications.
- Raw data or a full duplicate of business data is stored in a data lake. In a data lake, data is kept similarly to how it is in a business system.
- A data lake may handle various sorts of data-related components, including data formats, data sources, connection information, data schemas, and authorization management.
Below are the key characteristics of a Data Warehouse:
- Acquire good comprehension of the global Data Warehousing sector and its business environment by studying extensive industry studies.
- Helps examine production methods, major issues, and techniques for reducing production damage through historical data.
- The data warehouse is non-volatile, which means that previous data is not lost when new data is added.
- Helps in the analysis of historical data and the interpretation of what occurred and when it occurred.
- A data warehouse focuses on decision-making, data modeling, and analysis.
- Excludes information that would not be relevant in the decision-making process in order to offer a clear and short summary of the issue.
To provide you with a better view of the key differences between the two subjects, please refer to the table below:
Data Lake vs. Data Warehouse Comparison Chart
How can Data Lake and Data Warehouse complement each other?
Data lakes are used to store vast volumes of data from a variety of sources at a low cost. Enabling data of any form save costs since data is more adaptable and scalable because it isn’t bound by a schema. Structured data, on the other hand, is easier to examine since it is cleaner and has a consistent format from which to search.
Data warehouses are highly effective for evaluating historical data for specific data decisions because they confine information to a schema. Moreover, in a data process, data lakes and data warehouses complement one another.
For instance, information from the firm will be quickly ingested and stored in a data lake. When a specific business challenge arises, a piece of the data from the lake that is determined relevant is retrieved, cleansed, and exported into a data warehouse.
Data Lake or Data Warehouse: How to choose the one that will benefit you the most?
It’s a widespread belief that data warehouses are better suited to small and medium-sized firms, but data lakes are more frequent in bigger organizations. However, the right choice is actually dependent on the type of data involved and the sources of those data.
But to help you choose which one best fits your needs, we’ve outlined some information below:
There’s no better way to choose which data storage platform best fits your company than to evaluate it based on your needs and business operation.
Furthermore, data lakes and data warehouses are two inseparable components that are extremely effective when both are utilized well.
Not to mention, data lakes are becoming more and more user-friendly while data warehouses continue to prove their worth in terms of data analysis and reporting.