How to Build a Unified, Scalable Cloud Data Lake

Large, flexible, and powerful.

No, not the Stay Puft Marshmallow Man.

Cloud data lakes can store large amounts of data, are flexible enough to store a wide range of structured and unstructured information, and are a powerful tool for business analytics across diverse industries.

This guide is the only one you’ll need to understand what a data lake is, why you may need one, and how to build and implement your own unified, scalable cloud data lake.

Key Takeaways:

  • Data lakes are different but not mutually exclusive from data warehouses.
  • You can build a cloud data lake by identifying data sources, storage, governance, AI, and ML.
  • A cloud data lake requires stages of implementation.

Data Lakes vs. Data Warehouses

Depending on your organization’s needs, you may need a data lake and a data warehouse. They aren’t mutually exclusive, but they share many important similarities and differences.

The aptly-named “data warehouse” is ideal for structured, relational data. In that way, they are similar to their analog counterpart: just like a brick-and-mortar warehouse stores boxes using a consistent, organized system and with a specific purpose in mind, a data warehouse stores relational data that it cleans, enriches and transforms for a specific purpose.

Compare that to a lake: it’s full of fish, frogs, plants, snakes, and maybe even some trash. The water can sometimes be murky, but the lake is varied. A data lake lives up to its namesake: it stores a wide range of structured, unstructured, relational, non-relational, curated, and non-curated data. While a data lake can be disorganized and opaque compared to its cousin, the data warehouse also serves a fundamentally different purpose.

Data warehouses are useful to business analysts and are well-suited to storing relational data from operational databases and transactional systems. They have a faster query time but at the price of more expensive storage.

Data lakes are useful to data scientists (as well as business analysts). Their larger repository of information is ideal for machine learning and storing data from social media, IoT devices, and software applications. While data lakes are slower than data warehouses, storage is cheaper.

You wouldn’t store boxes in a lake like you wouldn’t go fishing in a warehouse. Both data warehouses and data lakes serve essential but separate functions.

Identify Data Sources

Where is your data coming from? Identify your data sources and the frequency with which you receive data. Data lakes can use push-and-pull-based methods of ingesting data.

The biggest paradigm shift between data warehouses and data lakes is that data warehouses start by defining a schema and then fitting data into it, while data lakes work in the opposite direction to ingest data and then create a schema to accommodate it.

Your data sources can be IoT devices, applications, databases, or data warehouses themselves. A data lake doesn’t replace a data warehouse but can utilize the same data for different purposes. 

Data lakes bring data from various sources into one unified repository.

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Scalable Cloud Data Storage

There are innumerable ways to store your data, but the most popular storage solutions are Google Cloud Storage (GCS), Amazon Simple Storage Service (S3), and Microsoft Azure. These solutions are similar in what they can offer, so how do you choose the right one for you?

If you’re building a data lake, it should be there for the long haul. You want to scale it to meet your needs today and years down the line. Use a single cloud solution for your entire data lake. To create parity and cohesiveness across your organization, use your tech stack’s cloud solution. 

Data Governance

You’ve set up your storage and started to dump data into it—this is fine for a while but can quickly spiral out of control without data governance. While cloud data lakes are flexible enough to ingest all kinds of data, it’s important that you keep your data organized. Once you’ve used your data lake to transform and curate data, business users should feel confident in its integrity.

Data governance ensures that data is secure, accessible, and trustworthy. Building a data lake is about bringing large amounts of data into one place and ensuring that data is accurate and useful so that business users can make data-driven decisions.

Artificial Intelligence and Machine Learning

While humans find visual patterns, computers excel at finding patterns in large data sets. With the right data governance and error detection, a data lake is a powerful sandbox for AI and ML to explore.

AI and ML are vital to your data lake as tools like DataBuck detect errors early in the process. It can save you countless hours of fixing errors after they’ve had time to propagate and metastasize—by detecting errors early, you can have higher trust in reports and analytics, lower data maintenance costs, and increased efficiency in scaling.

That’s only the beginning of what AI and ML can do with your cloud data lake. One advantage of cloud data lakes over data warehouses is that data lakes collect more data—more types, more formats, and more volume.

This data repository enables AI and ML to perform big, deep learning and interactive analytics. The ultimate use of your cloud data lake is to increase the efficiency and profitability of your organization, and AI and ML are the tools that allow you to achieve this.

Stages of Building a Cloud Data Lake

Each component of a data lake works together to create accurate, accessible, actionable data. You can’t have data governance without data sources or data storage without data governance. It becomes a chicken-and-egg scenario, but these four stages of implementation can help you get started.

  • Stage 1: Landing zone for raw data. In this stage, your data lake is separate from core IT systems. You aim to capture and store raw data in a cost-effective, scalable iteration.
  • Stage 2: Data-science environment. In this stage, you begin data governance. You can begin to conduct tests on the raw data you collected in stage 1, and data scientists can start to build analytics tools.
  • Stage 3: Offload for data warehouses. Up until now, your cloud data lake has been experimental. At this stage, you connect your data lake to other data warehouses. You will extract and import large amounts of data to and from your cloud data lake.
  • Stage 4: Critical component of data operations. At the final stage of cloud data lake implementation, your data lake is a fully-connected. You implement full governance as data-intensive applications begin to use your data lake.
A data lake's implementation stages move from low to high integrity.

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Monitor Your Data with DataBuck

The risk of error grows with each piece of data your cloud data lake processes. Most companies monitor less than 5% of their data, resulting in expensive and frustrating mistakes. You don’t have to settle for 5%.

FirstEigen created DataBuck to eliminate unexpected errors and monitor data autonomously. As your business changes and grows, DataBuck can scale with you, ensuring you have access to valid, helpful data at all times.

DataBuck has helped organizations from top banks worldwide to leading telehealth providers and even municipal governments of major cities. To see how we can help you, contact us today to learn more.

Check out these articles on Data Trustability, Observability, and Data Quality. 

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