How to build a data lake

Data-driven companies that have embraced the data lake as their storage technology are outperforming their business like never before.

After all, to get the best out of data and thrive in this digital world, it is necessary to have well-selected and good quality technologies, which will enable your success in today's industry 4.0.

So, to future-proof your projects, do like great experts and revolutionize the way you structure and use data by building a data lake.

Check out now how to do it in four steps!

What is a data lake?

First, let's review the concept of a data lake – or, in Portuguese, data lake. It's basically a warehouse that allows you to store all types of data (structured, unstructured, and hybrid) in one place and at scale (its scalability is endless!).

Its main objective is precisely this centralization of all raw data so that it is available to the data team, which will work to obtain results and answers to new business opportunities as quickly as possible.

But be careful, because a data lake is only an effective tool if it is properly built.

And that's why, once again, we opened our doors and brought you the method we use here at Indicium, considered the most efficient by our expert team.

So, let's get to know the step-by-step process of building an infallible data lake?

How to Build a Data Lake in 4 Steps

It is already more than clear that the data lake is a dynamic and powerful tool that will provide valuable information, essential for the growth of your projects and for the advancement of the business in the Data Driven Journey.

However, creating a properly architected and controlled data lake in the cloud, despite being affordable, is not as simple as it seems. So now we're going to show you how to build a productive data lake in four steps.

Follow!

Step 1: Mapping the data sources

Building a data lake starts with clarifying what data the organization needs to collect and for what business purpose.

This is a step that consists of identifying the data sources needed for each new type of information that needs to be collected. It is an analysis task that requires communication between departments.

Want to know why?

Since collecting large volumes of data is not the goal in itself, you need to focus on the business goal. As such, some data may be more valuable than others.

In addition, care must be taken not to let the data lake become a data sw Portuguese amp, where data is inserted haphazardly, without context, and without generating value for your business.

And how to do it?

With communication! It's the key to data lake success. And here are some questions to ask for priority setting:

• Is the data tracked in log files?
• Are they updated in batches?
• Are they generated in an event stream?
• Is each activity sent separately as it is in the source of origin?
• Are there data stores that can be relational or structured?

Then, for each source you identify, you'll need to configure access to the data source environments. That's when other questions to ask arise, such as:

• Who are the administrators or owners of data origination environments?

With this information, you can already determine what data you really need, and you can communicate specific needs to data managers and owners.

An important tip for starting this process is to establish two plans to obtain the necessary data (one immediate and one future).

Step 2: Data ingestion

This is the stage where data collected from various sources is transferred to the data lake.

It's a very technical task that involves organizing and cataloging the data so that users know exactly what's stored in the data lake and can easily find and access it.

Here, you'll see that the information communicated in step 1 will be extremely helpful. This is because, for each type of data, there are some details that contribute to making your intake more productive. Here are some examples.

• For batch data: You must set up processes to schedule periodic file transfers or batch data extractions.
• For event data: You must set up processes to receive the events - this can be a terminal event - and even if there is a default event format (action, object), you can set up a receiver function that will transform all incoming events into the default format before sending them through the data lake fire hose.
• For log data: You must determine how long it will be available, for example, by setting it to expire after a certain period of time, and with that, you will need to ensure that all log history is preserved.

In addition to these specifics of data types, there are other important tasks in this ingestion step, such as:

• Configure the storage location in the data lake.
• Establish a consistent approach to bucket naming and storage.
• Define how you're going to handle the production, development, and testing environments, considering your source and internal environments in the data lake.
• Set up processes to bring in reference data (users, departments, calendar events, work project names).
• Consider other groups or departments that may be affected by any new processes in place and communicate changes proactively.

Step 3: Data Transformation

In the third step, the focus is on cleaning and organizing the data. That's when you'll start to tackle the best ways to combine data in a more meaningful way to serve downstream reports or dashboard queries.

To that end, there are five steps within this step, which we'll quickly summarize for you.

1. Find and determine common identifiers in the input data records.
2. Identify similar but differently named structures in the data fields, and define logic for any transformations that occur (parsing specific identifiers of string fields, for example).
3. Determine how to handle viewing fields that contain strings that may be too long or have characters that will not be supported.
4. Build and maintain a set of mapped tables, with local and global identifiers, to unify data across systems.
5. Maintain communication with any departments or groups that have (or can help locate) the source of origin to validate identifiers.

Step 4: Data Consumption

Finally, comes the stage where the data will be positioned in structures that are optimized for later use.

This is when query libraries are created and communicated to the departments and users who will benefit from them.

And this is also when tests are carried out to validate all the configurations made. Then, after this legitimization, they can finally be accessed in various forms by various business intelligence tools.

In addition, in this last step, there are some good practices to be carried out in finalizing a successful data lake.

For example, evaluate whether queries and visualizations can be stored in development programs that allow them to be shared and reused. This is good for connecting data science professionals to create prototypes and validate algorithms.

And finally, we emphasize: always maintain regular communication with data lake users to determine new requirements for new or extended data sources.

Why should you adopt the data lake in your projects?

Here at Indicium, we like to say that data lake projects are business projects, not IT projects. Therefore, you should only adopt a data lake if it's part of a larger project that aims to drive real results in your organization.

In other words, the data lake will be part of a complete modern data platform, designed based on the company's evolving needs in the Data Driven Journey.

So, follow these four steps and harness all the power that a data lake has.

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