Data Lake vs Data Warehouse vs Databases: Which Meets Your Storage Needs?
Databases, Data warehouses, and Data Lakes all have different purposes and use-cases, but understanding those differences isn't always easy. Here's a quick guide to help you navigate the sometimes-confusing world of cloud storage.
Organizations that achieve success harness the business value inherent in their data. When devising a successful big data strategy, a crucial initial step is selecting the appropriate technology to store, search, analyze, and generate reports from the data. In this post, we will address frequently asked questions about databases, data lakes, and data warehouses. We will explore their definitions, distinctions, and help you determine which option is most suitable for your needs.
But before we dive into the definitions Databases, data warehouse and data lakes, let's first understand What is a Data Store?
A data store is a digital repository that stores and safeguards the information in computer systems. A data store can be network-connected storage, distributed cloud storage, a physical hard drive, or virtual storage. It can store both structured data like information tables and unstructured data like emails, images, and videos. Organizations use data stores to retain, share, and manage information across business units.
Now that you have a clear understanding of what a data store is, let's embark on an exploration of databases, data warehouses, and data stores which are different types of data stores!.
A database is a collection of data or information. Databases are typically accessed electronically and are used to support Online Transaction Processing (OLTP). Database Management Systems (DBMS) store data in the database and enable users and applications to interact with the data. The term “database” is commonly used to reference both the database itself as well as the DBMS.
You may also find database characteristics like:
- Security features to ensure the data can only be accessed by authorized users.
- ACID (Atomicity, Consistency, Isolation, Durability) transactions to ensure data integrity.
- Query languages and APIs to easily interact with the data in the database.
- Indexes to optimize query performance.
- Full-text search.
- Optimizations for mobile devices.
- Flexible deployment topologies to isolate workloads (e.g., analytics workloads) to a specific set of resources.
- On-premises, private cloud, public cloud, hybrid cloud, and/or multi-cloud hosting options.
If your application needs to store data (and nearly every interactive application does), your application needs a database. Applications across industries and use cases are built on databases. Many types of data can be stored in databases, including:
- Patient medical records
- Items in an online store
- Financial records
- Articles and blog entries
- Sports scores and statistics
- Online gaming information
- Student grades and scores
- IoT device readings
- Mobile application information
While selecting the database we should also look into the data access patterns, understandably, the number of transactions, atomicity, consistency, isolation, and durability (ACID) compliance, traffic patterns(burst reads or consistent reads), latency, and access requirements for a globally distributed application in order to identify the optimal storage solution.
It is also important to analyze various query patterns, random access patterns, and one-time queries when selecting a database solution. Additionally, it is crucial to consider specialized query functionalities for tasks such as text and natural language processing, time series analysis, and graph data. It is worth noting that not all databases support these functionalities, and many NoSQL databases employ an eventual consistency model.
A wide variety of databases exist. Examples include:
- Relational databases: Amazon RDS for Oracle, Amazon RDS for MySQL, Amazon RDS for SQL Server, and Amazon RDS for PostgreSQL
- Document databases: Amazon DocumentDB
- Key-value databases: Amazon Elasticache and Amazon DynamoDB
- Graph databases: Amazon Neptune
A data warehouse is a system that stores highly structured information from various sources. Data warehouses typically store current and historical data from one or more systems. The goal of using a data warehouse is to combine disparate data sources in order to analyze the data, look for insights, and create business intelligence (BI) in the form of reports and dashboards.
You might be wondering, "Is a data warehouse a database?" Yes, a data warehouse is a giant database that is optimized for analytics.
Data warehouses store large amounts of current and historical data from various sources. They contain a range of data, from raw ingested data to highly curated, cleansed, filtered, and aggregated data. Extract, transform, load (ETL) processes move data from its original source to the data warehouse. The ETL processes move data on a regular schedule (for example, hourly or daily), so data in the data warehouse may not reflect the most up-to-date state of the systems. Data warehouses typically have a pre-defined and fixed relational schema. Therefore, they work well with structured data. Some data warehouses also support semi-structured data.
As data warehouses are aimed at storage and analysis of large amount of information and the data warehouse helps to promote decision-making capabilities for reporting, analyzing on different aggregate levels using Online Analytical Processing. OLAP, short for Online Analytical Processing, is a technology that facilitates exploration and analysis of large datasets in a multidimensional way. Instead of looking at data from a single perspective, OLAP enables users to view it from multiple dimensions.
Once the data is in the warehouse, business analysts can connect data warehouses with BI tools. These tools allow business analysts and data scientists to explore the data, look for insights, and generate reports for business stakeholders.
Data warehouses are a good option when you need to store large amounts of historical data and/or perform in-depth analysis of your data to generate business intelligence. Due to their highly structured nature, analyzing the data in data warehouses is relatively straightforward and can be performed by business analysts and data scientists.
Note that data warehouses are not intended to satisfy the transaction and concurrency needs of an application. If an organization determines they will benefit from a data warehouse, they will need a separate database or databases to power their daily operations.
Amazon Redshift is a data warehouse developed by Amazon Web Services. Some popular data warehouse tools are Xplenty, Teradata, Oracle 12c, Informatica, IBM Infosphere, Cloudera, and Panoply and offerings other cloud service providers like Azure Synapse.
A data lake is a repository of data from disparate sources that is stored in its original, raw format. Like data warehouses, data lakes store large amounts of current and historical data. What sets data lakes apart is their ability to store data in a variety of formats including JSON, BSON, CSV, TSV, Avro, ORC, and Parquet.
Typically, the primary purpose of a data lake is to analyze the data to gain insights. However, organizations sometimes use data lakes simply for their cheap storage with the idea that the data may be used for analytics in the future.
Data lakes store large amounts of structured, semi-structured, and unstructured data. They can contain everything from relational data to JSON documents to PDFs to audio files. Data does not need to be transformed in order to be added to the data lake, which means data can be added (or “ingested”) incredibly efficiently without upfront planning. The primary users of a data lake can vary based on the structure of the data. Business analysts will be able to gain insights when the data is more structured. When the data is more unstructured, data analysis will likely require the expertise of developers, data scientists, or data engineers. The flexible nature of data lakes enables business analysts and data scientists to look for unexpected patterns and insights. The raw nature of the data combined with its volume allows users to solve problems they may not have been aware of when they initially configured the data lake.
Data in data lakes can be processed with a variety of OLAP systems and visualized with BI tools like Amazon Quicksight.
Data lakes are a cost-effective way to store huge amounts of data. Use a data lake when you want to gain insights into your current and historical data in its raw form without having to transform and move it. Data lakes also support machine learning and predictive analytics.
Like data warehouses, data lakes are not intended to satisfy the transaction and concurrency needs of an application.
Data lakes can provide storage and compute capabilities, either independently or together. Amazon S3 provides flexible and scalable storage for building data lakes. Other technologies enable organizing and querying data in data lakes, including, AWS Athena, Amazon Lakeformation and AWS Glue.
Companies are producing an ever-increasing volume of data through smart devices, the Internet of Things (IoT), social media, and various other sources. As a result, customers are seeking effective methods to manage and store this data. They often utilize databases, data warehouses, and data lakes, either individually or in combination, to store and analyze their data efficiently. Nearly every interactive application will require a database. When organizations want to analyze their data from multiple sources, they may choose to complement their databases with a data warehouse, a data lake, or both. When determining if a data lake and/or data warehouse is right for your organization, consider the following questions:
Is my data structured, semi-structured, or unstructured? Data warehouses support structured and semi-structured data whereas data lakes support all three.
Will my analysis benefit from having a pre-defined, fixed schema? Data warehouses require users to create a pre-defined, fixed schema upfront, which lends itself to more limited (but easier) data analysis. Data lakes allow users to store data in its raw, original format, which makes it easier to store data without having to apply and maintain structure.
Where is my data currently stored? Data warehouses require you to create ETL processes to move your data into the warehouse. Depending on where the data is stored, a data lake may not require any data to be moved. For example, Amazon Glue is able to access data stored in an Amazon S3 bucket, which can be quite advantageous for organizations who are already storing their data there.
In this following video, Temi delves into the concepts, advantages, and practical applications to assist you in selecting the optimal storage solution for your specific data requirements.
Databases, data warehouses, and data lakes each have their own purpose. Nearly every modern application will require a database to store the current application data. Organizations that want to analyze their applications' current and historical data may choose to complement their databases with a data warehouse, a data lake, or both.
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