What’s the Difference Between a Data Lake and Data Warehouse?
Do you know the difference between a data lake and a data warehouse? If not, you’re not alone — many believe these terms are interchangeable.
But these two terms actually represent distinctly different concepts. A lake and a warehouse for data are both frequently used for storing big data, but that’s where their similarities end.
To better understand the difference between the two, let's take a look at what each of these vital storage entities in the data world is, and how each works.
What’s a Data Warehouse? How Does It Work?
A data warehouse embodies the traditional, established, and proven repository for storing structured, processed data. Data warehouses are known for reliable performance, security, and more.
In a warehouse, data is stored to provide accessible storage for frequently-accessed structured data and cost-efficiency for housing structured data that is accessed infrequently.
Having processed data in multiple databases that are each housed in a SQL environment is perfect for running queries for fast, relevant results as the data warehouse optimizes data query speeds by moving frequently-accessed information into fast storage.
The data within the databases is then organized in a table format that can be customized by adding various descriptors. Each table can be organized into schemas.
Query tools in SQL use these schemas to select the data tables to analyze for the most relevant results, providing informative data for decision making.
However, there are two considerations your organization should make before utilizing this data storage method:
- Massive storage capacity also requires a large database server, which can potentially put a dent in your budget. Conversely, data warehouses in the cloud can handle big data with ease, allowing you to run tried and true analytics and business intelligence on terabytes of data without any issues. Cloud data warehouses can also scale according to an organization’s ever-increasing data needs.
- With machine learning and data science developing into mainstream business methodologies and tools for using both structured and unstructured, raw and curated data, a platform different than the data warehouse has become necessary, which is where the data lake comes into the picture.
How Is a Data Lake Different?
The most important factor about a data lake is that all data can be found there — the good, the bad, and the ugly.
Data lakes aggregate data of all types from various sources into a single repository, each type in its original format. This includes raw, unstructured data, which might have both structured data (i.e. data that has been extracted from an enterprise application and underlying database such as CRM and ERP) inside of it; unstructured data (i.e. text documents, manuals, interactions logs from the company's website or call logs captured from the contact center); or even semi-structured data (i.e. hybrid data, such as support tickets with both structured data and text narrative).
Data lakes represent a newer concept compared to data warehouses, as they address many big data challenges while working on a wide, “store everything” basis and handling heavy volumes of various types of data. This relational and non-relational data can come from sources such as:
- IoT Devices.
- Mobile Apps.
- Social Media.
- Corporate and Line-of-Business Applications.
What Are the Pros and Cons of a Data Lake?
One of the most popular benefits of a data lake is that your organization can store all of its data within it. With proper metadata management, it can hold data usable for machine learning and other important purposes, as well as scale any amount of data in your lake without structuring it — and can keep it as long as necessary.
When the data is needed, you can run various analytics against it, including big data processing, machine learning, and even dashboards or visualizations — all without moving it to an analytics platform. The data is easily accessible and can be used with a variety of analytic tools, including open-source frameworks such as Amazon Athena©, Amazon Redshift©, and Amazon QuickSight©.
As with data warehouses, data lakes are cloud-friendly and can be spun up with minimal data management or access control work. With some additional effort, you can catalog the data in the lake and utilize it in more advanced applications, such as machine learning.
One of the greatest drawbacks of a data lake is that without proper data pipeline management and cataloging, you can easily end up with a data swamp that is difficult to use and lacks real value. While it’s easy to add data to the lake, it can be tougher to sift through all of that information to find what exactly you need.
A data process framework for your business needs is essential for utilizing the data lake — and preventing the creation of a “data swamp.” Having well-defined data catalogs for your data lake will help data scientists and stakeholders use the data for business benefit.
Lastly, data lakes generally require more storage space than data warehouses since they are used to store all of your organization’s data, including unstructured data such as images and videos.
Should I Use a Data Lake or a Data Warehouse?
Considerations for a Data Lake
To create and effectively use a data lake, you will follow the steps below, using many of the available Amazon tools and services:
- Import data into your data lake. Move your data into your new data lake, housed with the help of the Amazon S3© object storage service. For a simple start on your data lake journey, you can also consider using Amazon’s AWS Lake Formation© to easily set up a new, secure data lake within the S3 service by defining data sources, accesses, and security policies. Lake Formation will help you collect and catalog data from databases and object storage, move the data into your new data lake, clean and classify it using machine learning algorithms — and secure access.
- Catalog your data. Manipulate, secure, and catalog your data using AWS Glue©, a serverless data integration service to easily discover, prepare, and combine your data in preparation for analytics, machine learning, and application development. Your data engineers — and your extract, transform, and load (ETL) developers can visually create, run, and monitor workflows with a few clicks using AWS Glue Studio©. Data analysts and data scientists can leverage AWS Glue DataBrew© to visually enrich, clean and normalize data without writing code. With AWS Glue Elastic Views©, your applications development team can use familiar SQL statements to combine and replicate data across different data stores.
- Perform data analytics. Use Amazon QuickSight© to achieve scalable, serverless, embeddable, machine learning-powered business intelligence results. Your organization may also want to consider:
- Amazon Athena©, which is an interactive query service to easily analyze and visualize your data in Amazon S3 using standard SQL queries.
- Amazon SageMaker©, which is built on two decades of experience developing real-world machine learning applications, including product recommendations, personalization, intelligent shopping, robotics and voice-assisted devices.
Considerations for a Data Warehouse
If you need a processed data structure, currently-used data readily available, are targeting business professionals — and don’t object to the more complicated accessibility and cost to make changes, a data warehouse may be a better option.
To prepare to create your warehouse, remember that data flows in from various sources such as transactional systems, relational databases, and others, typically at regular intervals. Business analysts, data engineers, data scientists, and decision makers can access the data using business intelligence (BI) tools, SQL clients, and other analytics applications.
To build and use a data warehouse, you will take the steps below, using available Amazon tools and services:
- Create an Amazon Redshift cluster. Use the AWS Management Console to begin.
- Configure the cluster. Choose the instance type and specify the number of nodes.
- Secure your cluster. Use AWS IAM and set it up for access.
- Load sample data to your cluster. Pull the data from Amazon S3 after defining a schema and creating your data tables.
- Download and use the Amazon Redshift JDBC driver. This will allow you to access your data in the Amazon Redshift cluster.
Analyze the data. Amazon QuickSight©, a cloud-native, serverless business intelligence service with native ML integrations and usage-based pricing, allows insights for all users. You can, in a single data dashboard, include AWS data, third-party data, big data, spreadsheet data, SaaS data, B2B data and more.
So, What’s a Data Lake House?
A lake house approach considers the idea that a “one-size-fits-all” approach to analytics eventually leads to compromises — and with data volumes increasing exponentially, exploding from terabytes to petabytes and even exabytes, traditional on-premises data analytics approaches don’t scale well enough to manage the data volumes.
Rather than simply integrating a data lake with a data warehouse, this methodology considers integrating a data lake, a data warehouse, and purpose-built stores, enabling unified governance and easy movement of data.
You may wish to build a data lake house to obtain possibly the best combination of options, including a data lake, a data warehouse, and purpose-built data stores when indicated — and a set of tools to manage everything all in one place without having to move around data.
With an AWS lake house architecture, you can store data in a data lake, circling the lake with a ring of purpose-built data services in order to be able to make quick and agile decisions at great scale and performance with a lower cost. To create a lake house architecture, you will need to:
- Rapidly build scalable data lakes.
- Use a broad and deep collection of purpose-built data services.
- Ensure compliance in a unified way to secure, monitor, and manage access to your data.
- Scale your systems at a low cost without compromising performance.
In terms of architecture and building, tools you can use to architect and build data analytics for your data lake house include S3, AWS Lake Formation, Amazon Athena© and Amazon Redshift. AWS Glue and AWS Glue Elastic Views© will handle Seamless Data Movement while AWS Lake Formation manages your row-level security.
Are You Ready To Kick Start Your Use of Data Lakes?
No matter which solution is right for your organization, let Onix and Amazon Web Services help you every step of the way through your data modernization journey.
Learn more about how we can help your organization get started with successful data migration today!
Dive into the many other services AWS offers with these blogs in our AWS 101 series:
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- AWS 101: How Does Amazon EC2 Work in Cloud Computing?
- AWS 101: How AWS Identity and Access Management (IAM) Works
- AWS 101: How AWS Cloud Security Securely Protects Your Data
- AWS 101: Why You Should Be Deploying AWS Lambda to Run Code
- AWS 101: Using AWS Auto Scaling to Manage Infrastructure
- AWS 101: What is Amazon Route 53?
- AWS 101: What is Amazon S3 Glacier Storage?
- AWS 101: A Look at AWS Database Services
- AWS 101: Understanding Amazon Elastic Block Store
- AWS 101: Monitoring with Amazon CloudWatch