AWS Data Lakes: A Deep Dive into Amazon Lake Formation Service

AWS Data Lakes
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In this day and age, data is everything. Your cloud infrastructure, environment setup, the security you have in place—all of it revolves around working with and protecting the precious data you have.

For businesses, data can come from various sources; it can be raw, structured, unstructured, etc. All of this data has value, or at least potential value, and many storage solutions exist to accommodate it. In this article, we’re going to look at a somewhat new storage solution called a data lake. We’ll also discuss AWS Lake Formation, a cloud service designed specifically for creating and working with data lakes.

Data Lakes: What They Are and Why We Need Them

Many of you have probably never heard of the term “data lake,” so let’s start by explaining what it is. A data lake represents a kind of centralized data repository, where you can store all of your data (whether structured or unstructured) at almost any scale. This data can later be used for analytics, machine learning, big data processing, visualization, etc.

While a data lake may sound like a messy place, it’s anything but. A data lake contains information that has value (or at least potential value) along with some sort of screening process to ensure that no junk is hoarded. Also, data lakes use a system of organization, which is very important when keeping such a vast collection of data.

Data lakes can be extremely useful, as they allow you to identify business growth opportunities faster and boost productivity. With it, you can improve your research and development choices by allowing your teams to test their ideas and assess the results. You can also improve customer interaction by combining a different array of customer data, from shopping history to social media inputs, and use it to increase customer satisfaction. And with the Internet of Things collecting data from a multitude of sources, you can also increase your operational efficiencies, making your business more profitable.

Losing Sight of What’s Important

If your data lake is poorly organized or contains too much “junk,” it is no longer a data lake; instead, it is referred to as a “data swamp.” And as you can guess, aside from other issues that may arise, data swamps can be unnecessarily costly.

To ensure that your data lake remains “clean,” there are a few things you need to be mindful of. First, as a business, reduce the collection of useless data as much as possible. Today, it is very simple to store data, and this freedom to keep everything has put companies in a disadvantageous position, allowing them to hoard information that serves no purpose other than to increase costs and render their data lake ineffective. Also, it is extremely important to keep the lifecycle of data in mind. All data that is stored should be used for a purpose and then either archived or destroyed (unless you have other needs for it). Automation comes in very handy here, and you should try to implement it as early as possible.

Data Lakes vs. Data Warehouses

As you may expect, there’s an inevitable comparison to be made between data lakes and data warehouses, but keep in mind that they are two completely different things. Data lakes store a mix of various data (both relational data from various business applications and non-relational data from social media, IoT devices, etc.), where the structure of data itself is not defined. So your entire data collection is stored without a specific design or even upfront knowledge of how you’ll exactly be using that data.

Data warehouses, on the other hand, are very strict. They represent optimized databases that look at relational data coming from your business applications. Here, data structure is known and defined in advance so that fast SQL queries can be handled. You know exactly why you need this data and how you will use it.

Why Data Lakes Fail

The first reason data lakes usually fail is because of the above mentioned issue of data swamps. After unnecessary hoarding takes place, and all structure and organization is lost, a data lake becomes much less useful and reliable, and users simply stop using it.

Data volumes are another issue. While data lakes are supposed to contain large amounts of information, having to parse through all of it is a challenge—and for some, it’s a challenge they cannot handle.

Another reason behind data-lake failure is because businesses fail to properly utilize this data for analytical purposes. This often happens when data becomes stale, thanks to the slow nature of business processes, and is no longer valuable. In many cases, this then leads to the analytics produced by the data lake not having the expected impact, causing businesses to re-evaluate the use of data lakes altogether.

AWS Lake Formation

So, now that you understand what data lakes are, let’s take a look at AWS Lake Formation.

AWS Lake Formation is a service that allows you to get a data lake up and running in the Amazon cloud. It does this by organizing various AWS tools (such as AWS Cloud Backup) into one orchestrated service, meaning AWS Lake Formation is basically a wrapper that glues many other services together in order to present you with a functional data lake. This service isn’t necessary (as you can do all this by yourself), but it certainly helps you remove the massive overhead required by this process. For example, creating a data lake involves running services like IAM, S3, SQS, SNS, etc., and configuring all of these simply takes up your valuable time.

AWS Lake Formation works by utilizing a preconfigured set of templates, which are used to bring up all the abovementioned AWS services quickly and coherently. You can also modify these templates to tailor them to your specific needs. To create a data lake using AWS Lake Formation, you just need to define the data sources as well as security policies to be applied. The service will collect all the existing data for you and move it to your new data lake, stored in S3.

But while AWS Lake Formation does a great job of creating a functional data lake for you, it does only that—and nothing else. To have a data lake that is actually useful, you need to have an entire pipeline in place, including active data ingestion and data analytics to produce some value. None of this is going to be created for you, so there is still some manual work that has to be done. How you set up your data ingestion and whether you will rely on machine learning, Athena, Amazon Redshift, Amazon EMR, or something else is entirely up to you.

AWS Lake Formation itself comes at no additional cost—being a wrapper service, there is nothing to charge. But you will be paying for all the services that were brought up using AWS Lake Formation, so keep that in mind.

Solving your big data challenges with AWS Data Lakes

As you can see there are loads of benefits to deploying AWS Data Lakes in the cloud (improved elasticity, security, deployment time, availability and cost effective storage growth just to name a few), as well as a few downsides, particularly if your data lakes are poorly organized. As useful as data lakes can be, it is very important to keep in mind that their value can decrease very quickly if they aren’t utilized correctly. We also reviewed AWS Lake Formation, an AWS managed service that takes all the necessary services to run a data lake and packages and configures them for you.

While not a complete solution, AWS Lake Formation is a great place to start, and with a bit of additional work, you can have your data lake environment up and running fairly quickly. If you are running your business on the AWS cloud, and data lakes are providing value to your company, we encourage you to experiment with AWS Lake Formation.

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