An Overview of Data Lakes, Use Cases, and Corresponding Tools on AWS 

Tue, 12/20/2022 - 09:21

SMBs are increasingly leveraging data lakes for advanced and real-time analytics

A data lake is a centralized repository that allows businesses to store structured and unstructured data at any scale. It provides a single source of data that can be analyzed and used to support a variety of business needs, such as reporting, analytics, machine learning, and more.

Small to midsize businesses (SMBs) are increasingly turning to data lakes as a way to store and analyze large volumes of data in a cost-effective and scalable manner. These companies are using data lakes to gain insights and make data-driven decisions that can help them improve operations, drive growth, and stay competitive.

Benefits of data lakes

One of the primary benefits of data lakes for SMBs is their ability to store and process large amounts of data from a variety of sources. This includes structured data, such as transactional data from CRM or ERP systems, as well as unstructured data, such as social media posts, website logs, and sensor data. By bringing all of this data together in a single repository, SMBs can gain a more comprehensive understanding of their business and customers.

Another key advantage of data lakes is their ability to support real-time analytics and decision-making. By using tools like streaming analytics, SMBs can analyze data as it is being generated, allowing them to quickly respond to changing market conditions and customer needs.

SMBs are also using data lakes to support machine learning and artificial intelligence (AI) initiatives. By storing and processing large amounts of data in a data lake, SMBs can train machine learning models and use AI to automate tasks and make more informed decisions. For example, an SMB in the retail industry might use a data lake to train a machine learning model that can predict customer purchase behavior and make recommendations for personalized marketing campaigns. 

Data lakes also offer SMBs a high level of flexibility and scalability. They can easily add new data sources and applications as their needs change, and they can scale their data storage and processing capabilities up or down as needed.

AWS tools for data lakes

One of the key tools for implementing data lakes on AWS is Amazon S3 (Simple Storage Service). S3 is a highly scalable, secure, and durable object storage service that makes it easy to store and retrieve data from a data lake. S3 can handle any type of data, including structured, unstructured, and streaming data, and it can be accessed from anywhere using standard web protocols.

In addition to S3, AWS offers a range of other tools and services that can be used to build and manage data lakes. For example, Amazon Athena is a serverless query service that allows businesses to analyze data stored in S3 using SQL. Amazon EMR (Elastic MapReduce) is a fully managed big data processing service that makes it easy to process and analyze data stored in S3 using open-source frameworks like Apache Spark and Hadoop.

Integration and data management tools can be used to build and maintain data lakes. For example, Amazon Glue is a fully managed extract, transform, and load (ETL) service that can be used to move data between S3 and other data stores. Amazon Lake Formation is a fully managed service that makes it easy to build, secure, and manage data lakes.

One of the key benefits of implementing data lakes on AWS is the ability to scale storage and processing resources up or down as needed. Businesses can easily add or remove storage and processing capacity as their data volume and processing needs change, allowing them to optimize costs and minimize waste.

Getting started

Data lakes are becoming an increasingly important tool for SMBs looking to leverage the power of big data and analytics, but having the resources to get started can hold many companies back. When working with Stratus10, we help you:

  1. Determine data sources, such as databases, log files, and social media platforms.
  2. Choose a data lake storage solution, such as S3 and consider factors like the size and format of data, budget, and level of security and durability needed.
  3. Set up data ingestion processes from various sources. This may involve tools such as AWS Data Pipeline or AWS Glue to extract, transform, and load (ETL) your data.
  4. Choose a data processing and analysis solution like Amazon EMR or Redshift. We take into consideration the size and complexity of your data, your budget, and the types of analysis you want to perform.
  5. Set up data governance and security with tools like AWS Lake Formation to manage access to data and implement data lineage tracking.
  6. Make data accessible to users, which may involve Amazon QuickSight or SQL-based tools to query and visualize data in your data lake.

By leveraging the power of data lakes, businesses can store and process large volumes of data in a cost-effective and scalable manner, and gain insights and make data-driven decisions that can help them improve operations, drive growth, and stay competitive.

Learn more about cloud infrastructure in AWS Best Practices: Components of the 3-tier Infrastructure.

Could you benefit from a consultation on data lakes for your company? Get in touch with a Stratus10 cloud expert today!

Call us at 619.780.6100
Email us at
Fill out our contact form
Read our customer case studies

Newsletter Sign Up