Data Lake Patterns – Integrating Your Data across the Enterprise

Today, technology companies like Amazon, Google, Netflix, and Facebook drive immense success as they can get insight from their data and understand what customers want. They personalize the experience in front of you, for example, movie suggestions from Netflix, shopping suggestions from Amazon, and search selections from Google. All of their success is credited to being able to dig into data and utilize that for customer engagement. That’s why data is now considered the new oil.

Picture this – you are getting ready to watch television, excited to see your favorite show. You sit down and try to change the channel, only to find that the remote control is not working. You try to find batteries. You know you have some in the house but you don’t remember where you put them. Panic sets in, and you finally give up looking and go to the store to get more batteries.

A similar pattern repeats...

Definition of a data lake

Data is everywhere today. It was always there, but it was too expensive to keep it. With the massive drops in storage costs, enterprises keep much of what they were throwing away before. And this is the problem. Many enterprises are collecting, ingesting, and purchasing vast amounts of data but need help to gain insights from it. Many Fortune 500 companies are generating data faster than they can process it. The maxim data is the new gold has a lot of truth, but just like gold, data needs to be mined, distributed, polished, and seen.

The data that companies are generating is richer than ever before, and the amount they are generating is growing at an exponential rate. Fortunately, the processing power needed to harness this data deluge is increasing and becoming cheaper. Cloud technologies such as AWS allow us to process data almost instantaneously and in a massive fashion.

A data lake is an architectural approach that helps you manage multiple data...

The purpose of a data lake

You might not need a data lake if your company is a bootstrap start-up with a small client base. However, even the smaller entities that adopt the data lake pattern in their data ingestion and consumption will be nimbler than their competitors. Especially if you already have other systems in place, adopting a data lake will come at a high cost. The benefits must outweigh these costs, but this might be the difference between crushing your competitors and being thrust into the pile of failed companies in the long run.

The purpose of a data lake is to provide a single store for all data types, structures, and volumes, to support multiple use cases such as big data analytics, data warehousing, machine learning, and more. It enables organizations to store data in its raw form and perform transformations as needed, making it easier to extract value from data. When you are building a data lake, consider the following five V’s of big data:

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Components of a data lake

The concept of a data lake can vary in meaning to different individuals. As previously mentioned, a data lake can consist of various components, including both structured and unstructured data, raw and transformed data, and a mix of different data types and sources. As a result, there is no one-size-fits-all approach to creating a data lake. The process of constructing a clean and secure data lake can be time-consuming and may take several months to complete, as there are numerous steps involved in the process. Let’s take a look at the components that need to be used when building a data lake:

• Data ingestion: The process of collecting and importing data into the data lake from various sources such as databases, logs, APIs, and IoT devices. For example, a data lake may ingest data from a relational database, log files from web servers, and real-time data from IoT devices.
• Data storage: The component that stores the raw data in its original...

Data lakes in AWS with Lake Formation

Lake Formation is a fully managed data lake service provided by AWS that enables data engineers and analysts to build a secure data lake. Lake Formation provides an orchestration layer combining AWS services such as S3, RDS, EMR, and Glue to ingest and clean data with centralized fine-grain data security management.

Lake Formation lets you establish your data lake on Amazon S3 and begin incorporating readily accessible data. As you incorporate additional data sources, Lake Formation will scan those sources and transfer the data into your Amazon S3 data lake. Utilizing machine learning, Lake Formation will automatically structure the data into Amazon S3 partitions, convert it into more efficient formats for analytics, such as Apache Parquet and ORC, and eliminate duplicates and identify matching records to enhance the quality of your data.

It enables you to establish all necessary permissions for your data lake, which will be enforced across...

Data lake best practices

In this section, we will analyze best practices to improve the usability of your data lake implementation that will empower users to get their work done more efficiently and allow them to find what they need more quickly.

Centralized data management

Depending on your company culture, and regardless of how good your technology stack is, you might have a mindset roadblock among your ranks, where departments within the enterprise still have a tribal mentality and refuse to disseminate information outside of their domain.

For this reason, when implementing your data lake, it is critical to ensure that this mentality does not persist in the new environment. Establishing a well-architected enterprise data lake can go a long way toward breaking down these silos.

Centralized data management refers to the practice of storing all data in a single, centralized repository rather than in disparate locations or silos. This makes managing, accessing, and...

Key metrics in a data lake

Now more than ever, digital transformation projects have tight deadlines and are forced to continue doing more with fewer resources. It is vital to demonstrate added value and results quickly.

Ensuring the success and longevity of a data lake implementation is crucial for a corporation, and effective communication of its value is essential. However, determining whether the implementation is adding value or not is often not a binary metric and requires a more granular analysis than a simple “green” or “red” project status.

The following list of metrics is provided as a starting point to help gauge the success of your data lake implementation. It is not intended to be an exhaustive list but rather a guide to generate metrics that are relevant to your specific implementation:

• Size: It’s important to monitor two metrics when evaluating a lake: the overall size of the lake and the size of its trusted zone...

Lakehouse in AWS

A lakehouse architecture is a modern data architecture that combines the best features of data lakes and data warehouses, while a data lake is a large, centralized repository that stores structured and unstructured data in its raw form. To have a structured view of data, you need to load data into the data warehouse. The lakehouse architecture combines a data lake with a data warehouse to provide a consolidated view of data.

The key difference between a lakehouse and a data lake is that a lakehouse architecture provides a structured view of the data in addition to the raw data stored in the data lake, while a data lake only provides the raw data. In a lakehouse architecture, the data lake acts as the primary source of raw data, and the data warehouse acts as a secondary source of structured data. This allows organizations to make better use of their data by providing a unified view of data while also preserving the scalability and flexibility of the data lake...

Data mesh in AWS

While data lakes are a popular concept, they have their issues. While putting data in one place creates a single source of truth, you are also creating a single source of failure, violating standard architecture principles to build high availability.

The other problem is that the data lake is maintained by a centralized team of data engineers who may need more domain knowledge to clean data. This results in back-and-forth communication with business users. Over time your data lake can become a data swamp.

The ultimate target of collecting data is to get business insight and retain business domain context while processing that data. What is the solution? That’s where data mesh comes into the picture. With data mesh, you can treat data as a product where the business team owns the data, and they expose it as a product that can be consumed by various other teams who need it in their account. It solves the problem of maintaining domain knowledge while...

Choosing between a data lake, lakehouse, and data mesh architecture

In a nutshell, data lake, lakehouse, and data mesh architectures are three different approaches to organizing and managing data in an organization.

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. A data lake provides the raw data and is often used for data warehousing, big data processing, and analytics. A lakehouse is a modern data architecture that combines the scale and flexibility of a data lake with the governance and security of a traditional data warehouse. A lakehouse provides raw and curated data, making it easier for data warehousing and analytics.

A data mesh organizes and manages data that prioritizes decentralized data ownership and encourages cross-functional collaboration. In a data mesh architecture, each business unit is responsible for its own data and shares data with others as needed, creating a network of data products...

Summary

In this chapter, you explored what a data lake is and how a data lake can help a large-scale organization. You learned about various data lake zones and looked at the components and characteristics of a successful data lake.

Further, you learned about building a data lake in AWS with AWS Lake Formation. You also learned about data mesh architecture, which connects multiple data lakes built across accounts. You also explored what can be done to optimize the architecture of a data lake. You then delved into the different metrics that can be tracked to keep control of your data lake. Finally, you learned about lakehouse architecture, and how to choose between data lake, lakehouse, and data mesh architectures.

In the next chapter, we will put together everything that we have learnt so far and see how to build an app in AWS.