A data lake is a single place to store a lot of data. It lets you abstract bits of information from many different places without having to structure the information into a rigid form.
Think of an e-commerce business. Your business can gather customer profiles, orders, product details, clicks on your website, the searches they perform, interactions you have with them via customer support, and product images, and that at a single place.
The structure is different for each of the datasets. One way you can have all of these datasets collected within a single large environment is through a data lake. The information can be saved in advance and then organized later. This means your data engineers can provide datasets according to the needs of your business. As a result, a data lake can be the starting point for numerous contemporary data workloads.
How Does a Data Lake Work?
A typical data lake architecture starts with data ingestion. There are multiple sources of your data. These sources might be databases, applications, APIs, CRMs, websites, enterprise software, and IoT devices.
There are batch and stream processes that can bring data into your lake:
- When data comes in at regular intervals, you can consider batch ingestion. You could, for example, report sales to your lake daily.
- Streaming ingestion is different and continually adds events to your environment. This technique works for site events, application events, and sensor data.
- Once ingested, it is possible to store the data in its original form. Then, your data engineering team can clean, transform, validate, and mash up the data as necessary.
This is referred to as schema-on-read. You do not set up a hard structure for storing the data, but apply the structure when you process or analyze it.
What Information is Possible to Store in a Data Lake?
The best benefit of a data lake is that its structure is flexible.
Not just traditional rows and columns: Data of different kinds can be stored in the same environment. Your data lake may include:
- Customer and transaction records
- File formats that are supported: JSON, XML, CSV
- The application and server logs
- Images, videos, and documents
- The Internet of Things and sensor data
- Machine learning datasets
Data lakes are useful for organizations that collect data from multiple and isolated systems. With a shared source for data, there’s no need for separate storage space for each format. Let’s start by comparing the two.
Data Lake vs. Data Warehouse
The distinction between a data lake and a data warehouse can be relative based on data storage and data usage.
Usually, a data warehouse is associated with structured and processed information. Typical warehouse applications include reporting, dashboards, financial analysis, and BI (business intelligence).
A data lake provides more flexibility. Raw information can be saved alongside processed datasets. Various data formats can be used, too.
This is not to imply that a data lake is always meant to take the place of a data warehouse. A few organizations do. You can store raw data and divergent data in your data lake and structured data in your warehouse for reporting and business intelligence.
It is dependent on methodologies and approaches to your data sources, workloads, security needs, and business objectives.
Why Do Businesses Use Data Lakes?
As your business expands, your data environment generally gets more complicated. Beginning with customer and sales data is a good idea. After that, you could include marketing data, application events, support data, IoT data, and machine learning data.
It may be expensive and challenging to manage these sources individually.
A data lake offers a single place to consolidate all this data. Your teams can then work with massive data sets without having to trawl through and repeat the process of transferring data from one system to another. Data lakes can also serve as a way to enable experimentation.
A retailer could, for instance, merge purchase information with website activity, interaction with customers, and product information. This information can be used by data scientists to analyze customer patterns and develop customer recommender models.
What Are the Benefits of a Data Lake?
A good data lake provides versatile storage options for different sorts of data. Can also grow with the amount of data. This is easier with cloud-based data lakes. The storage space can be expanded without the need to continuously invest in physical storage facilities.
Another advantage is ease of access.
Information can come from a variety of business systems, and your analysts, engineers, and data scientists can access it. This allows teams to use big data to create analytics and machine learning applications.
But storage does not create value.
With a data lake, your data needs pipelines, ownership, governance, and security. If these are missing, it may be challenging to manage the environment.
What Is Data Lake Governance?
Data lake governance is the way your organization is governing and securing data. Definitions about access, security, ownership, quality, and usage must be made. Your teams can also use metadata and data catalogs to become familiar with the contents of different datasets. Of course, security becomes even more important if your lake has important information about your customers or business.
Owners of particular datasets need to take measures to make sure they can only be accessed by the right people. It is also important to watch over key data elements and establish suitable data retention policies.
If it’s not managed effectively, your data lake can turn into what many teams refer to as a data swamp! It is not like there is a lack of data. The difficulty is that information that’s stored often doesn’t get easily found, understood, or trusted.
Data Lakes and Modern Data Platforms
Data lakes are being made more powerful with modern technologies. Lakehouse architectures integrate lake and database features, like data lakes and data warehouses, and can be enabled with platforms such as Databricks.
A Databricks lakehouse enables data engineering, analytics, artificial intelligence, and machine learning on a single platform. This can minimize data latency between the data environments.
A data lake can be reflected as a modern data platform in addition to being a data store.
When Should You Consider a Data Lake?
If you have multiple data sources, a data lake may be of benefit. It also works well when you have a rapidly expanding number of data records or when you are looking to enable more complex analytics and machine learning.
It can also be advantageous if your data formats change frequently. But, even when you have a lot of data, do not go ahead with a data lake. Begin by determining your real needs. Take into account the data sources, processing workloads, governance and security needs, and growth plans.
The goal of your architecture should be to address a genuine business challenge.
Conclusion
A data lake is a flexible data repository that can feature a wide variety of data.
Data can be stored in any combination of structured, semi-structured, and unstructured data within a single environment. This information can be ingested by your teams for analytics, AI, machine learning, and other workloads.
It’s the systems that are built around that storage that really matter. It’s important to have a strong data lake architecture, pipelines that are reliable, governance, security, and data quality. In combination, your data lake can be a key ingredient to your modern data strategy. Find top Databricks Consulting companies for better implementation and to build and optimize your data architecture.
