Why You Need a Data Lake in 2023

It's no secret that artificial intelligence (AI) and machine learning (ML) are two of the most exciting (and controversial) technology trends happening today. AI adoption is skyrocketing across the business world, with around 9 in 10 organizations believing that AI technologies will give them a competitive edge in a fiercely competitive business landscape .

But investing in AI and ML is more complex than buying off-the-shelf products and hitting 'go' or coming up with an excellent automation project idea. Instead, quality data and reliable data infrastructure is the key to getting the most out of your AI projects. With this in mind, let's dive into why you need a data lake and how data engineering can help prime your data assets for AI success.

However, many organizations lack the data infrastructure required to build compelling automated solutions. This data infrastructure includes data lakes, data lakehouses, and data warehouses (more on this in the next section). And hiring even the most experienced data scientists and analysts won't solve this problem. This is where data engineering comes in.

Data engineering is the practice of designing systems to collect, store, manage, and analyze data at scale. Without data engineering, data scientists and analysts don't have the ingredients they need to build robust machine learning models or data analysis tools effectively. Data engineers make raw data usable for data scientists.

Here's the bottom line. If an organization lacks a data engineering strategy, the data they collect is essentially useless. For this reason, data engineering is paramount to successful AI and ML projects.

What Is a Data Lake? And Why Are They Important?

Data lakes are a critical tool in the data engineer's arsenal. A data lake is a centralized storage repository that allows you to store large amounts of data regardless of that data's format, source, size, or structure. This data can be structured, semi-structured, or unstructured.

Structured data has a high degree of organization and is typically stored in a spreadsheet-like manner. Examples include .csv files, excel spreadsheets, and relational database tables. Semi-structured data has some degree of organization but less so than structured data. HTML files, JavaScript Object Notation (JSON) files, and XML files are examples of semi-structured data. By contrast, unstructured data has no pre-defined organizational form or specific format. Examples of unstructured data include images, videos, sound files, PDFs, plain text files, and more.

The primary purpose of a data lake is to provide a single source of truth from which data teams can leverage data for various business use cases. Since data lakes can rapidly ingest all types of new organizational data, businesses can respond to new information faster. Data lakes also provide access to data and insights businesses couldn't access in the past due to informational silos (disparate IT systems).

Data warehouses and data lakehouses also fit into this picture, although there are some critical differences between the three architectures. Data warehouses are rigid and normalized, with well-structured and easily readable data. Compare this to a data lake, where the data can be structured but can also be raw, loosely bundled, and decoupled.

But what's a data lakehouse? A data lakehouse is a new, open data design architecture that combines the benefits of a data lake (flexibility and cost-efficiency) with the benefits of a data warehouse (data management and structure features).

Benefits and Use Cases of Data Lakes

Combining Batch and Real-Time Data For Business Insights

Organizations today generate huge amounts of batch and real-time data from different sources and in various formats. By building a data lake, they can combine this data to start building AI models and analytics platforms that drive better business outcomes.

Crucially, data lakes form a critical part of high-performance architectures for applications that rely on new or real-time data, including predictive decision engines, recommendation systems, cybersecurity threat detection tools, and fraud detection tools.

Unlocking Insights Without a Goal

Since data lakes don't demand a pre-defined schema, they can store raw data regardless of whether you have a specific purpose in mind for this data. This means you can unlock hidden insights by using this data for training statistical models for classification, clustering, detection, and prediction in machine learning projects, or something else entirely.

A Single Source of Truth

Let's say a company creates web applications based on remote sensing models (satellite images). In this scenario, the company has a lot of different data in various formats and sizes that they need to manage. This task becomes even more complex due to the siloed nature of departments within the organization. For example, the IT team may be in charge of some data, while domain experts and analysts are responsible for others.

A data lake helps combat this problem by providing a single source of truth for all data, bridging the gaps between departments, and breaking down silos. With a data lake, each department can receive the data they need quickly and easily and be confident in the integrity of the data.

 

Final Thoughts

Data lakes, data warehouses, and data lakehouses, and the data engineering technologies that enable them, lie at the heart of all successful machine learning and artificial intelligence projects. If you want to accelerate your automation projects and rapidly gain compelling business insights, we can help build the infrastructure you need. This infrastructure will be a core system that drives your future development and empowers data scientists to work their magic. Get in touch today.