Data lakes and data warehouses play a big role in the data analytics space. Companies are increasingly powered by their data. To leverage all the data at your fingertips, you’ll need to consider the best way to store and structure it. Two solutions are a data lake and a data warehouse — but which one should you choose?
The short answer: you may need both. For many companies, it’s not a matter of data warehouse vs data lake because they each serve distinct purposes and use cases.
To make the right choice, you’ll first need to answer basic questions like, “What is data lake?” and “What are the differences between a data warehouse vs data lake?” Let’s look at each in more detail so you can apply them in the right scenarios.
Table of Contents
Data Lake Definition: What is a Data Lake?
Why Use a Data Lake?
How Do Data Lakes Work?
Data Lake Architecture: Components of a Data Lake
Are Data Lakes Secure?
Where Are Data Lakes Stored?
Define: What Is a Data Warehouse?
What Is The Difference Between a Data Warehouse and a Data Lake?
Data Lake vs Data Warehouse: Which One Do You Need?
Solving Your Data Lake Questions with Meltwater
Data Lake Definition: What is a Data Lake?
Let’s define the meaning of data lakes: What is a data lake?
A data lake is a centralized source where you store your data. It doesn’t require any special structure or organization. Rather, you can feed all of your data — structured data and unstructured data — into the data lake without having to first tweak and refine it.
Despite the lack of structure, users can run different types of data lake analytics to gain insights. These might be dashboards, visualizations, or machine learning — all of which leverage the data within the lake to derive business value.
Why Use a Data Lake?
The open format that data lakes offer allows businesses to capitalize on their data without having to structure it first. This opens a wide range of possibilities to learn from your data. It removes siloes and boundaries that may prevent you from getting holistic insights.
They’re also cost-effective and will scale with your business. They don’t require the same management and maintenance as a data warehouse (more on that in a moment), giving you a shortcut to insights.
What’s more, data lakes enable technologies like machine learning, deep learning, and advanced analytics to thrive. You don’t know what you don’t know, which is where machine learning shines brightly. It can connect the dots between unstructured or semi-structured data points you didn’t realize mattered. This helps you get more from your data and make better business decisions.
How Do Data Lakes Work?
Data lakes allow you to collect data from multiple sources, such as machine-to-machine, logs, business applications, IoT-connected devices, social media, website sessions, and more. You can collect data in real time or feed data into the lake in batches. All of these data sources feed into the same lake in their original formats.
There’s no need to define a specific schema, nor are there limitations as to the size of the data. Once collected, users can crawl, index, and catalog data to understand what type of data is at their disposal.
Data Lake Architecture: Components of a Data Lake
To fully understand the data lake architecture, it helps to know its various components.
These include:
Data Ingestion
Ingestion refers to the sources of data and how they feed into the lake.
You may have multiple ingestion types, methods and sources:
- Ingestion Types
- unstructured data
- emi-structured data
- structured data
- Ingestion Methods
- batches
- real-time
- one-time
- Ingestion Sources
- IoT
- web servers
- ...
Data Security
Security protocols exist at every layer of the data lake.
These protocols help to prevent unauthorized access to data and may include authentication, protection, and authorization features.
Data Governance
Governance refers to how the data is managed, made usable, secured, verified, and made available to users.
Good data governance improves data quality, ensuring users can extract business value from the data.
Data Discovery
Before users can analyze Big Data, the data first needs to go through the discovery phase.
Data scientists can apply a tagging technique to organize and understand data feeding into the data lake.
Data Lineage
Tracking the movement of data is an important part of the data lake infrastructure.
Finding the data’s origin point and tracking where it moves over time helps to identify errors and ease the process of correcting them.
Data Auditing
Data auditing allows data scientists to evaluate risks and maintain compliance.
This involves tracking changes to data sets and learning who is making these changes and how and when the changes occur.
Are Data Lakes Secure?
Data lakes contain a variety of raw data, some of which may be sensitive (such as financial information). However, data lakes provide a secure data storage solution, thanks to a myriad of features and protocols.
For starters, data scientists can set up a data lake with role-based access and authentication phases. Users only have access to the data they need.
Other protocols, such as automatic data monitoring and data encryption, are also key parts of the infrastructure. This way, data scientists and IT teams know when data has been accessed or changed without authorization or when authorized parties perform suspicious activities.
Where Are Data Lakes Stored?
Though data lake solutions can be configured for on-premises operation, cloud data storage is best for data lakes. The scalable storage offered by cloud technologies share similar characteristics.
Enterprise-grade cloud platforms like Amazon are built for storing massive data lakes (think petabytes of data!). They also make for easier data management, as you can monitor access, security, costs, and other resources from a central source.
Define: What Is a Data Warehouse?
By definition, a data warehouse is a type of database that caters to relational data that stems from transactional applications. This type of data is structured and allows users to make quick queries to use for reporting purposes.
What Is The Difference Between a Data Warehouse and a Data Lake?
We’ve covered specifics like the data lake definition and how data lakes work. But how do data lakes compare to data warehouses?
For starters, data lakes deal with more types of data than data warehouses. Data warehouses stick to structured relational data from business applications. Data lakes can store this data, too, but it can also store non-relational data from apps, internet-connected devices, social media, and other sources.
The data in a data warehouse follows a specific schema. The goal is to provide a single source of truth, so data must be cleaned and transformed prior to users accessing it. Data lakes do not rely on any specific schema.
Users that typically have specific questions to ask and answer will benefit from the structure of the data warehouse. With data lakes, users may or may not know the questions they need to answer, now or in the future. It’s like saving data for a rainy day.
There are also differences in cost, data quality, end users, and use cases. We’ve given a brief breakdown below:
| Comparison Variable | Data Warehouse | Data Lake |
|---|---|---|
| Data types | Structured relational data from business applications and transactional systems | Relational and non-relational data from business data applications and other sources |
| Schema | Schema-on-write — designed prior to implementation | Schema-on-read — designed as the data is requested by the end user |
| Scalability | Harder to scale because of increasing storage costs | Easier to scale regardless of amount or type of data |
| Cost | Higher storage costs | Lower storage costs |
| Speed | Fast query results | Slower query results |
| Ease of use | Easy due to the defined structure and query speed | Difficult due to the vast amount of data and lack of structure |
| Data quality | High-quality results; can be trusted as a single source of truth | Varying quality — raw data and multiple sources are not highly curated |
| End users | Typically used by business analysts | Typically used by data scientists, business analysts, and data teams |
| Use cases | Business Intelligence (BI) applications and visualizations | Predictive analytics, machine learning, and data discovery |
Data Lake vs Data Warehouse: Which One Do You Need?
Choosing between a data warehouse vs data lake requires more than knowing the data lake meaning and differences between a lake and a warehouse. Most importantly, what’s in it for you?
More companies are shifting from the data warehouse to the data lake due to its ability to uncover hidden insights and extract more business value. Companies can harness more information from more sources using a data lake. This allows departments to collaborate more effectively and improve decision making across the enterprise.
For instance, data lakes can prove useful in interfacing with customers. By combining data from your CRM and social media channels, sales and marketing teams can learn more about buying histories, spending patterns, and support needs to tailor their offerings and improve services.
Connected data may also help companies to improve product development and operational efficiency. Conduct faster research to increase your speed to market and understand what customers are willing to pay to price your new offerings attractively, for example.
However, that’s not to say that data warehouses no longer have a place in your big data strategy. Companies may see more value when they have both, as each serves different end users and use cases.
Solving Your Data Lake Questions with Meltwater
For monitoring media coverage and achieving holistic insights into your online presence, Meltwater takes the data lake approach. Our cloud-native technology ingests data from a myriad of sources, including social media channels, blogs, podcasts, news outlets, and more, to help you keep tabs on your brand.
Our approach starts by collecting online events, then enriching them with data and details to provide context around each event. Our data platform stores structure data at scale for the long term, allowing you to gain historical insights and track your performance over time.
Learn more about how Meltwater is empowering your PR and comms with data when you request a demo.
