Comparison Between Data Lakes, Data Warehouses, & Databases

December 2, 2020

Most of todays organizations collect and process large amounts of data daily. But this data can only bring value if the organization derives valuable insights from the data.

A major factor in the success of a big data strategy is choosing the right technology to enable data storage, search, analysis, and report generation.

Depending on the data, the scope, and how it’s used, an organization has the option to invest in a data warehouse, data lake, or database.

This article will go over data lakes, data warehouses, databases, their use cases, and future outlook to help you decide which best suits your goals, data strategy, and infrastructure.

Overview of data lakes

A data lake is a centralized storage repository that allows the storage of structured, unstructured, and semi-structured data.

Data lakes allow you to store data in its original format (raw form) without the need of transforming or processing it at the time.

It’s much cheaper and flexible to store data in a data lake than in a data warehouse. Despite this, you should not replace a data lake with a relational database or data warehouses. Relational databases use a structure that enables easier access and information identification concerning other data pieces in the database.

Some of the most popular data lakes are Amazon S3, Google Cloud Storage, and Apache Hadoop.

Overview of data warehouses

A data warehouse refers to a storage location that hosts large amounts of structured data, from one or more sources, in a centralized place.

Unlike a data lake that stores unprocessed data, data warehouse stores already filtered data processed for a specified purpose.

Data warehousing includes the construction and usage of databases. Data warehousing also involves data consolidations, data integration, and data cleaning.

Data warehouse construction includes the integration of data from multiple heterogeneous sources. It must support decision making, analytical reporting, and structures or ad hoc queries.

Mid-size organizations and large-size businesses rely on data warehousing to share content and data across departments-or in teams-siloed databases.

These organizations invest in data warehouses because of their ability to generate business insights across the business teams.

Amazon Redshift, Snowflake, Yellowbrick, Teradata, Microsoft Azure Synapse, and IBM Db2 Warehouse are examples of cloud database solution providers.

Overview of databases

A database is a storage repository that houses structured data. All related data is stored collectively in a database to ensure easy information access, management, and updating. A database has only one major purpose of storing data.

Data was stored on tapes, mostly write-only, during the early computer days. These storage systems were bulky and slow; thus, data scientists saw the need for a modern database management system.

A database management system (DBMS) includes hardware, software, procedures, data, and database processing language as its components. With a DBMS, you can create, manipulate, and define a database, allowing you to easily store, analyze, and process data.

Besides, DBMS ensures the security and protection of databases and maintains data consistency for multiple users.

Some of the most popular DBMS are MongoDB, Oracle, Apache Cassandra, PostgreSQL, Elasticsearch, and Redis.

Industrial use cases of data warehouses

Industries that process large amounts of data find data warehousing most applicable to their needs. These include governments and companies in the insurance, healthcare, education, and finance industries.

Healthcare

Healthcare institutions collect a lot of information from patients, suppliers, and other stakeholders daily. Once collected, the information is organized and stored in a data warehouse.

These organizations can access this data at a later date to predict epidemics in advance, create treatment plans, and strategize on purchases.

Public sector

The public sector relies on data warehouses for intelligence gathering. Government agencies maintain and analyze citizens’ records relating to health, tax records, etc.

Industrial use cases of data lakes

A data lake complements a data warehouse by providing additional query options.

While data warehouses provide organized and structured information, the addition of a data lake helps organizations tap into raw data.

Offering more support and insights, considering that data lakes facilitate real-time analytics. Data lakes are applicable in IT, research, and science, among other industries.

Research and science

The field of science is ever-evolving, and the use of real-time data helps predict and deduce critical insights.

Data lakes are applicable for scientific use because they hold raw data from algorithms and feedback sources in real-time.

Scientific developments rely on the most current and relevant deductions to produce impactful findings and reports. Data lakes help in this regard because they allow the data importation in real-time.

Information technology

Data lakes enable information technology architects to access data in its most original form.

They can case this data depending on their needs.

Organizations use raw data to create more effective products that meet customers’ expectations.

Industrial use cases of databases

Databases are mostly used in data science research and testing. Engineers and data scientists are the primary users of databases.

Business data analysis

Databases have excellent reporting features and are useful for data analysis and trend predictions.

For instance, a sales promotion report may show that sales for an item, say A, increased following an effective email promotion.

Those of an item, say B, rose following an effective in-store promotion.

A productivity report may show that productivity slows drastically on the afternoon of the day before a holiday; the employer might (deduce), that it’s best to let the employees go early on the eve of holidays.

Telecommunication

Telecommunication companies use databases to store and generate customer bills, balances for prepaid customers, call logs, among other essential information.

Sales

A sales department benefit significantly from a company’s database. Among other tasks, sales teams use databases to track sales, product performance, and customer information.

The future of data lakes, data warehouses, and databases

The current shift towards cloud-based data platforms to mitigate data issues and manage data suggests that data lakes’ will continue growing deeper in the cloud.

The cloud environment enables faster deployment, reliability, scalability, and performance. It also offers access to analytic engines, especially those that analyze data from internet of things (IOT) devices.

Scientists expect future data warehousing to offer a more effective platform to integrate and work with data.

This may lead to warehousing systems that’ll allow users to leverage integration to generate more insight from their data without necessarily depending on a complicated data infrastructure.

The next-generation data warehousing will embrace simplicity in terms of ease of use and offer more data delivery capabilities.

There is a need to address the broader range of workloads in databases.

Data scientists anticipate that the leading cloud database platforms will address this need. General-purpose databases can now support data visualization, multiple data models, in-memory storage, distributed storage, and extended capabilities like graphs and spatial.

Next-generation databases will ensure data utilization in real-time. Data scientists will need to generate more accurate forecasts and computations.

Conclusion

Every organization needs to use the best data analytics system to address their needs and get the best outcome.

Databases, data lakes, and data warehouses provide most of the analytics solutions for today’s businesses.

With insights from this article, you will make the right choice between the three analytics solutions.


Peer Review Contributions by: Lalithnarayan C


About the author

Eric Kahuha

Eric is a data scientist interested in using scientific methods, algorithms, and processes to extract insights from both structural and unstructured data. Enjoys converting raw data into meaningful information and contributing to data science topical issues.

This article was contributed by a student member of Section's Engineering Education Program. Please report any errors or innaccuracies to enged@section.io.