Companies are collecting more data than ever. However, much of that data is unstructured, meaning it hasn’t been arranged according to predetermined models.
So how do you store and organize the data you’re receiving? How can you analyze your data and draw more meaningful insights?
Using a data lake can help.
In this article, we’ll explain what a data lake is and look at its architectural components. We’ll also look at different use cases for data lakes and how they compare to data warehouses.
What Is a Data Lake?
A data lake is a centralized repository designed to hold a vast amount of data. It can store three types of data:
Structured data: Data that has been formatted into a repository, such as a database. Examples include names, addresses, and phone numbers.
Unstructured data: Data that hasn’t been arranged in a predefined manner. Examples include text, documents, and Internet of Things (IoT) sensor data.
Semi-structured data: Data that doesn’t reside in a database but has some structure to it. Examples include HTML code, graphs, and spreadsheets.
The term “data lake” was originally coined by James Dixon, CTO of Pentaho, in 2010. Here’s what he said:
“If you think of a datamart as a store of bottled water, cleansed and packaged and structured for easy consumption - the data lake is a large body of water in a more natural state. The contents of the data lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples.”
The amount of data that is created and consumed is growing at a staggering rate. It’s also “flowing in” from different sources — databases, web services, data sensors, clickstream logs, etc.
In 2020, as much as 64.2 zettabytes of digital data were created. However, less than 2% was actually saved and retained.
Data lakes enable you to collect and store data from any system at scale. It can come from on-premise or cloud-based systems.
Some use cases for data lakes include:
Construction: Construction firms can use a data lake to build a historical database and use it to help with bidding and cost estimates.
Architecture: With a unified view of their data, architecture and engineering firms can perform real-time analysis and track key metrics across the building lifecycle.
Financial services: Investment firms can store their data in a central repository and use it to perform forecasts, assess risks, and inform investment decisions.
Data Lake Architectural Components
Data lakes offer a cost-effective way to store vast amounts of data, but they also have several challenges in terms of their data architecture design.
A typical data lake architecture consists of the following components.
Data ingestion pipelines
Data ingestion pipelines move data from one or more sources into a data lake. Ensuring data quality is a must, as simply dumping your data can make it challenging to organize and analyze later.
Data storage
Data storage enables companies to store vast quantities of data. However, it should support various data formats and provide fast access while remaining cost-effective.
Data governance
Data governance refers to setting internal standards and creating a data strategy for how data should be processed. A data lake without data governance can become inaccessible or provide little value to its users.
Data security
Data lakes should restrict access to trusted users only. It should also have additional authentication, encryption, logging, and auditing features.
Data exploration
Data exploration involves analyzing data in your data lake. This is done through the use of data visualization software, which allows you to manipulate your data, identify patterns, and derive valuable insights.
Data Lake vs. Data Warehouse: Pros and Cons
These are two types of data storage that are often confused with another: Data lakes and data warehouses. Both are used to store data, but they work in different ways.
Here are some of the pros and cons of each storage type.
Pros and cons of a data lake
A data lake is a large repository of raw data. It’s a place where you “dump” data in its original format without applying any data models to it.
The pros of a data lake include:
Volume and velocity: Data lakes can store massive amounts of data — a must for artificial intelligence and machine learning. They’re also designed for rapid data ingestion.
Lower costs: Data lakes are more cost-effective and easier to get started with. They’re also highly scalable, as they can grow with your data needs.
Greater accessibility: Data in a data lake is typically stored in a flat structure, which is easier to access and query when needed.
The cons of a data lake include:
Increased complexity: Deploying a data lake is straightforward enough with solutions like Amazon AWS and Azure Data Lake. But an on-premise deployment is significantly more complex.
Higher learning curve: Data lakes need more specialized tools and technical expertise to navigate, which usually requires a skilled data scientist.
Data integrity risks: Another downside is the potential to turn a data lake into a “swamp” of messy and unorganized data. This can happen with poor data management.
Pros and cons of a data warehouse
A data warehouse is a repository for structured and filtered data that has already been processed for a specific purpose. This allows for much faster querying.
The pros of a data lake include:
Faster retrieval: Data warehouses use a schema-on-write process, enabling their query engine to sort through data and generate faster insights.
More user-friendly: Because the data has already been processed, business users can quickly access and retrieve the data they need.
Increased flexibility: Companies can deploy data warehouses in the cloud or on-premise with SQL server stacks.
The cons of a data warehouse include:
Higher storage costs: Storage costs for data warehouses are more expensive than for data lakes. Maintenance costs are also much higher.
More time-consuming: Data that goes into a data warehouse must be processed first. However, data cleansing can be an incredibly time-consuming task without the right tools.
Limited data exploration: You could unintentionally limit the insights you're deriving because you’re only importing processed data.
Every company has different data needs. Ideally, you’ll make use of both data storage types as each serves different purposes.
How Toric Can Help
Data lakes enable your company to store volumes of structured and unstructured data from different sources in one place where it can be accessed and analyzed. Of course, you’ll need the right tools to ensure you’re getting the most out of your data.
Toric is a powerful data platform that lets you consolidate your data in one workspace. Build real-time data visualizations with data apps, create shared views, and more.
Request a demo today to see how you can leverage Toric in your company to turn your data into actionable business insights.
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