Building a Data Lake Using AWS Cloud Services

Man swimming in scuba gear in binary computer code to illustrate data lake architecture

Forward-focused organizations always look for ways to keep data clean, organized, and centralized. But doing this can be difficult when data is housed in disparate systems, platforms, and locations. Establishing a data lake architecture makes it possible to use, manage, store your complex information.

One way businesses combat this is by migrating multiple data sources to the cloud. A primary advantage of bringing data into the cloud is the ability to form a data lake. With these environments, organizations can support applications and analytics needs without increasing storage or computing demands, even as operations scale quickly.

However, reaping the savings and performance benefits from an AWS Cloud migration typically takes more than a simple “lift and shift” of your current architecture. Before migrating your data ecosystem to the cloud, you’ll need to invest in planning and proper data lake architecture design. It’s also wise to explore the available AWS Big Data services in detail to ensure the right fit for your organization.

Let’s explore the key technical considerations for building a data lake using AWS Cloud Services and what you need to know before migrating your data to the cloud.

What Is a Data Lake?

A data lake is a centralized repository that allows you to store all structured and unstructured data at any scale. The concept of a data lake originated in 2011, but it has already changed the foundational data strategy of organizations, both big and small. In a data lake, teams can store their raw data as-is, without strict, upfront structuring requirements. Then, they can use intuitive dashboards, visualization tools, and machine learning to guide them in making critical business decisions.

Well-functioning data lake architecture should support the following capabilities:

  • Collecting and storing any data type, at any scale, at a low cost
  • Protecting all of the data stored in the central repository
  • Locating relevant data within the central repository
  • Performing new types of data analysis on datasets quickly and easily
  • Querying the data by defining its structure at the time of use (schema on read)

How to Build Data Lake Architecture With AWS Cloud Services

Building a data lake leveraging Amazon Web Services (AWS) takes a defined storage solution, established data structure, the input of data, and ongoing management, processing, analysis, and security. It takes a team that has the knowledge and expertise in all of these areas to create data lake architecture that is flexible, scalable, and cost-effective.

Source: Building a Data Lake on AWS

When defining an AWS storage solution, there are several options you can go with. Xperity’s development experts understand these solutions and can guide teams in choosing the best one for their needs.

Here’s a list of the AWS storage solutions most commonly used for data lakes:

  • Amazon S3
  • Amazon EMR
  • Amazon Redshift
  • Amazon Athena
  • Amazon Redshift Spectrum
  • Other AWS Functionality

These solutions cleanse and standardize data in Amazon S3. Xperity teams also have the capability to build data lake architecture using AWS Cloud Services so users have in-depth context around system data.

The more information consolidated within a data lake and on hand, the more efficiently teams can transform raw data into a refined state for analytics or machine learning initiatives. This leads to data-driven decision-making that can improve operations and processes throughout an organization.

Benefits of an AWS Data Lake

Unlike traditional services, the AWS data lake architecture integrates with native AWS services that enable users to run big data analytics, artificial intelligence (AI), machine learning (ML), and other high-value initiatives. Through AWS, organizations can build data lakes around their unique needs with the ability to scale operations up or down as needed.

Here are some of the major benefits of an AWS data lake.

Cost-Effective Data Storage

Compared to traditional data warehousing options, Amazon S3 provides cost-effective and durable storage, allowing organizations to store nearly unlimited data of any type from any source.

Easy Data Collection and Ingestion

There are various ways to ingest data into your data lake:

  • Amazon Kinesis enables real-time ingestion
  • AWS Import/Export Snowball ingests data in batches
  • AWS Storage Gateway connects on-premises software appliances with your AWS Cloud-based storage
  • AWS Direct Connect gives you dedicated network connectivity between your data center and AWS
Scalability

Many organizations working with an Amazon data lake use the Amazon S3 because of its scalability. Data lakes can handle a vast amount of organizational data without the concern of storage limitations. This flexibility makes it easy to instantly scale-up storage capacity as an organization’s data requirements evolve.

Accessibility

Data lakes improve accessibility by allowing on-demand data access. Amazon Glacier and AWS Glue are all compatible with AWS data lakes and make it easy for end-users to access data and extract insights.

Security and Compliance

AWS data lakes operate on a highly secure cloud infrastructure with a deep suite of security measures created to protect your most sensitive data. This includes Amazon Virtual Private Cloud (VPC), Identity and Access Management (IAM), and AWS CloudTrail. These security features keep data safe from failures, errors, and threats, and security and compliance with industry standards.

Integrations with Third-Party Service Providers

Organizations can’t depend on a data lake alone. An AWS data lake allows businesses to leverage a wide variety of AWS Data and Analytics Competency Partners to add to their S3 data lake. This provides a range of value for third-party providers from easier data processing to analysis and visualization.

Fuel Data-Driven Decisions With Xperity

When implemented correctly, data lakes accelerate how organizations can use data to drive better decisions and results. Xperity optimizes and automates the configuration, processing, and loading of data into an AWS data lake.

Our data lake architecture approach eliminates time-consuming setup and management efforts. We help you quickly integrate with sophisticated BI tools like Tableau, Power BI, AWS Quicksight, and other modern analytics platforms.

Xperity also helps clients significantly reduce the total cost of ownership (TCO) by moving their data ecosystem to the cloud. Providers like AWS are continuously adding new services while also improving existing ones. It may seem like there’s an overlap in services in some cases, and it may be unclear which ones to use (i.e., AWS Glue vs. transient EMR) when building a data lake using AWS Services.

Xperity helps organizations develop robust data lakes and comprehensive data and analytics strategies that drive faster time-to-insights and business results. Our teams use their AWS data experience and analytics knowledge, and a defined pathway to project success that enables businesses to plan and execute their data-driven strategies efficiently and effectively.

Contact us to learn more and to see how The Xperity Performance Framework enables you to accomplish your development goals.

Article Summary

Data Lakes accelerate how organizations can use data to drive better decisions and results when implemented correctly. Xperity optimizes and automates the configuration, processing, and loading of data into an AWS Data Lake by using sophisticated BI tools.

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