What is a storage repository that holds a vast amount of raw data in its original format until the business needs it?

Data is the lifeblood of business. To help companies visualize their data, guide business decisions, and enhance their business operations requires employing machine learning services. But where to begin. Today, tremendous amounts of data are created by companies worldwide, often in disparate systems.

These large amounts of data, while helpful, don’t necessarily need to be processed immediately, yet need to be consolidated into a single source of truth to enable business value. Companies are faced with the issue of finding the best way to securely store their raw data for later use. One popular type of data store is referred to as a “data lake” and is very different from the traditional data warehouse.

Use Case: Data Lakes and McDonald’s

McDonald’s brings in about 1.5 million customers each day, creating 20-30 new data points with each of their transactions. The restaurant’s data comes from multiple data sources including a variety of data vendors, mobile apps, loyalty programs, CRM systems, etc. With all this data to use from various sources, the company wanted to build a complete perspective of a CLV and other useful analytics. To meet their needs for data collection and analytics, McDonald’s France partnered with 2nd Watch. The data lake allowed McDonald’s to ingest data into one source, reducing the effort required to manage and analyze their large amounts of data.

Due to their transition from a data warehouse to a data lake, McDonald’s France has greater visibility into the speed of service, customer lifetime value, and conversion rates. With an enhanced view of their data, the company can make better business decisions to improve their customers’ experience. So, what exactly is a data lake, how does it differ from a data warehouse, and how do they store data for companies like McDonald’s France?

What Is a Data Lake?

A data lake is a centralized storage repository that holds a vast amount of raw data in its native format until it is needed for use. A data lake can include any combination of:

• Structured data: highly organized data from relational databases
• Semi-structured data: data with some organizational properties, such as HTML
• Unstructured data: data without a predefined data model, such as email

Data lakes are often mistaken for data warehouses, but the two data stores cannot be used interchangeably. Data warehouses, the more traditional data store, process and store your data for analytical purposes. Filtering data through data warehouses occurs automatically, and the data can arrive from multiple locations. Data lakes, on the other hand, store and centralize data that comes in without processing it. Thus, there is no need to identify a specific purpose for the data as with a data warehouse environment. Your data, whether in its original form or curated form, can be stored in a data lake. Companies often choose a data lake for their flexibility in supporting any type of data, their scalability, analytics, machine learning capabilities, and low costs.

While data warehouses are appealing for their element of automatically curated data and fast results, data lakes can lead to several areas of improvement for your data and business including:

• Improved customer interactions
• Improved R&D innovation choices
• Increase operational efficiencies

Essentially, a piece of information stored in a data lake will seem like a small drop in a big lake. Due to the lack of organization and security that tends to occur when storing large quantities of data in data lakes, this storing method has received some criticism. Additionally, setting up a data lake can be time- and labor-intensive, often taking months to complete. This is because, when built the traditional way, there are a series of steps that need to be completed and then repeated for different data sets.

Even once fully architected, there can be errors in the setup due to your data lakes being manually configured over an extended period. An important piece to your data lake is a data catalog, which uses machine learning capabilities to recognize data and create a universal schema when new datasets come into your data lake. Without defined mechanisms and proper governance, your data lake can quickly become a “data swamp,” where your data becomes hard to manage, analyze, and ultimately becomes unusable. Fortunately, there is a solution to all these problems. You can build a well-architected data lake in a short amount of time with AWS Lake Formation.

AWS Lake Formation and Its Benefits

Traditionally, data lakes were set up as on-premises deployments before people realized the value and security provided by the cloud. These on-premises environments required continual adjustments for things like optimization and capacity planning—which is now easier due to cloud services like AWS Lake Formation. Deploying data lakes in the cloud provides scalability, availability, security, and faster time to build and deploy your data lake.

AWS Lake Formation is a service that makes it easy to set up a secure data lake in days, saving your business a lot of time and effort to focus on other aspects of your business. While AWS Lake Formation significantly cuts down the time it takes to set up your data lake, it is built and deployed securely. Additionally, AWS Lake Formation enables you to break down data silos and combine a variety of analytics to gain data insights and ultimately guide better business decisions. The benefits delivered by this AWS service are:

• Build data lakes quickly: To build a data lake in Lake Formation, you simply need to import data from databases already in AWS, other AWS sources, or from other external sources. Data stored in Amazon S3, for example, can be moved into your data lake, where your crawl, catalog, and prepare your data for analytics. Lake Formation also helps transform data with AWS Glue to prepare it for quality analytics. Additionally, with AWS’s FindMatches, data can be cleaned and deduplicated to simplify your data.
• Simplify security management: Security management is simpler with Lake Formation because it provides automatic server-side encryption, providing a secure foundation for your data. Security settings and access controls can also be configured to ensure high-level security. Once configured with rules, Lake formation enforces your access controls. With Lake Formation, your security and governance standards will be met.
• Provide self-service access to data: With large amounts of data in your data lake, finding the data you need for a specific purpose can be difficult. Through Lake Formation, your users can search for relevant data using custom fields such as name, contents, and sensitivity to make discovering data easier. Lake Formation can also be paired with additional AWS services, such as Amazon Athena, Amazon Redshift, and Amazon EMR. For example, queries can be run through Amazon Athena using data that is registered with Lake Formation.

Building a data lake is one hurdle, but building a well-architected and secure data lake is another. With Lake Formation, building and managing data lakes is much easier. On a secure cloud environment, your data will be safe and easy to access.

2nd Watch has been recognized as a Premier Consulting Partner by AWS for nearly a decade and our engineers are 100% certified on AWS. Contact us to learn more about AWS Lake Formation or to get assistance building your data lake.

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