The Five Elements of Cloud Data Lake Deployments

In the age of Big Data, it’s not just the volume of data in the world that’s exploding (over 1 trillion new MBs every day) – it’s also the variety of data that we have at our disposal. The rise of the Internet of Things (IoT), smartphone adoption, cloud computing, and other factors have enabled organizations to understand the world better using all types of information – digital images, physical documents, videos, sensor readings – the list goes on and on.

Therefore, having data infrastructure that can scale and handle a wide range of data types is table stakes in the modern world. Organizations that want to gain a competitive edge and offer better services must be able to collect and leverage data from a multitude of sources. This is where data lake technology comes in.

Data lakes are repositories for storing raw, unrelated information in one place. However, to describe data lakes only as a form of data storage would come up short. Data lakes allow teams to consolidate and standardize unstructured information to prepare it for advanced analytics. In other words, data lakes are crucial for helping companies discover deeper insights about their customers and operations.

With this background in mind, here are the questions you need to consider for any data lake deployment:

• Where is your data coming from?
• How will you ingest it?
• Where do you consolidate it?
• How do you process it?
• How do you analyze it?

These questions point to the five elements of a standard data lake deployment. But the standard doesn’t cut it in today’s competitive landscape. In our view, deploying data lakes on the cloud with AWS is the best approach.

Data Lake Sources

A data lake source can be anything that generates information worth gathering and analyzing. This can include software applications, smart devices, mobile phones, physical documents – virtually any asset that creates or holds information. Thanks to edge computing and the IoT, our list of data lake source possibilities is only getting longer.

Data Ingestion: Push or Pull

After identifying your sources, the next step is to determine how you will ingest data into your data lake. Common ingestion patterns include using APIs, FTP, JDBC, or even manual data uploads. What’s important is knowing whether your sources require you to “push” or “pull” your data into your data lake, as well as having the ability to automate this process.

Data Storage: Amazon S3 – The Heart of the Data Lake

At the center of any AWS data lake deployment is an Amazon S3 bucket for consolidating unstructured data. Anything collected in the field lands here and likely waits for some form of transformation or manipulation. Given that data lakes centralize raw and diverse data types, it’s expected that standardization is needed to prepare data for future use.

Extracting, Transforming, and Loading Data

Data lake data often goes through a series of transformations, moving stepwise through several Amazon S3 buckets. Eventually, this data is ready for extracting and loading into a database or data warehouse. Automation and efficiency are essential here, especially when working with big data volumes. Otherwise, costs can escalate quickly.

Data Analytics and Visualization

The last element in the cloud data lake ecosystem is the database or data warehouse that you use to mine your data for value. With modern analytics and visualization tools, it’s possible to identify patterns, unmet customer needs, and untapped niches from massive, diverse datasets that would otherwise go unnoticed in the pre-Big Data era.

ClearScale Data Lake Services

Altogether, these data lake ecosystem components enable organizations to create value in crucial ways. Data lake technology offers unlimited scalability, serverless IT operations, lower costs, and more. The challenge, of course, is knowing what tools and solutions to use at each stage to unlock the full potential of data lakes on the cloud.

At ClearScale, we help leaders deploy data lakes in a way that serves their overarching goals. This involves using powerful solutions from AWS that automate the data lake setup process, data ingestion, ETL, and analytics. We know how to use tools like AWS Lake Formation, AWS Glue, and AWS QuickSight that can integrate seamlessly into one sophisticated data lake implementation. With ClearScale, getting started with data lakes is fast and easy.

To learn more about cloud-based data lakes and how ClearScale can make it work for your specific needs, download our free eBook.

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