Data lake architecture is a centralized repository that stores large amounts of business data. It is a place where you can find different types of data, including semi-structured, structured, and unstructured data, in its native format. In other words, a data lake is a large container that is similar to a real lake.
Just like water comes into the lake from multiple tributaries, a data lake has unstructured data, structured data, logs, and machine-to-machine flowing through in real-time. Unlike a data warehouse where all the data from multiple sources is stored in files and folders, data lake architecture is flat. Each data element in the data lake has a unique identifier and metadata information.
In this data lake architecture guide, you’ll learn about the main components of architecture and how to build a robust and effective data lake.
Differences Between Data Lakes Vs. Warehouses
Data lakes are generally confused with data warehouses. Therefore, it’s crucial to understand the differences between data lakes and data warehouses.
- Data lakes can accumulate all the data from distinct data sources, whereas data warehouses focus only on business processes.
- Data lake frequently stores all types of data. On the contrary, a data warehouse has data in tabular form and structure.
- A data lake has a low-cost data store design, whereas a warehouse is an expensive storage medium.
- In a data lake, you have less control over security. On the other hand, data in a data warehouse offers better data control.
- Compared to data warehouses, a data lake is highly agile and can be configured and reconfigured as needed.
Understanding The Data Lake Architecture
Data lakes allow organizations to save time, money, and resources usually invested in data structure creation. This helps the in-house data scientists team to enable fast ingestion and data storage. Below are a few components of data lake architecture.
- Security: The ESG research reveals that the critical component of data lake flat architecture is its high security. However, it is different from security measures that are usually deployed for Relational Databases.
- Governance: The second component is vital in measuring performance and improving the data lake by monitoring and supervising operations.
- Metadata: It refers to other data, including but not limited to reload intervals, schemas, etc.
- Monitoring & ELT Processes: A simple tool is required to organize data flow moving from the raw layer to cleansed layer to the sandbox and application layer. While transferring data between the layers, sometimes you’ll need to apply transformations.
Now that you know the main components, here are the five layers of a typical data lake.
The purpose of the ingestion layer is to ingest raw data into a data lake without any modifications. In this layer, the raw data ingested in batches or real-time is organized into a logical folder structure.
Here the data stored in the ingestion layer is converted into the structured data format for data analytics. In this layer, the interpretation of raw data takes place and is transformed into structured data sets. All the demoralized, cleaned, and derived data becomes uniform in this layer.
The next layer of the data lake involves using user queries and advanced analytical tools on the stored structured data. You can run the processes in real-time, batch, or interactively. It is also called gold, trusted, or production-ready layer.
The insights layer acts as the query or output interface of the data lake. It uses SQL and NoSQL queries to fetch data from the lake and is generally executed by company users who need data access.
Unified Operations Layer
The last layer of data lake architecture is a unified operation layer. It monitors and manages the system using proficiency management, auditing, and workflow management.
Best Practices Of Data Lake Architecture
Now that you know every layer of the data lake architecture in detail, it’s time to learn the best practices for data lake building. Digital transformation demands knowing authentic and accurate data sources in the company. Some of the best practices for building an effective data lake strategy are as follows:
- Identify and define the data goal of your organization before you gather or store information.
- Traditional data architecture models are not sufficient. Therefore, you’ll need modern cloud data architecture with data profiling, data backup, cataloging, and securing data.
- Focus on data governance and metadata management to ensure data is clean, trusted, accessible, and secure.
- Automate data acquisition and transformation processes to build data lake architectural models faster.
- Integrate DevOps processes to build and maintain a reliable data lake. You need to establish clear guidelines about how and where you collect the data.
Build A Robust Data Lake With Inferenz Experts
Data storage is a storage platform where you can store all the different data. The main goal of building a data lake architecture is to offer an unrefined data view to data scientists.
As data volumes are increasing, updating data lake architecture is vital to suit the expectations of data-driven companies. Compared to data warehouses, data lakes are highly scalable in nature and help organizations scale their business in the competitive market.
If you’re looking to build an effective data lake architecture, feel free to contact Inferenz experts today.
FAQs About Cloud Data Lakes
What is a data lake?
A data lake is a single, centralized repository where the data team can find distinct data sources in one place. It can store all the raw data in the data lake and gives you the ability to understand how to use the data. AWS serverless data lake accelerates new data onboarding and helps organizations drive insights from the data collected.
Why build a data lake?
A data lake provides a large pool of storage to store data from different data sources in an organization. Here are the main reasons to store raw data into the data lake.
- A data lake provides a holistic view of data and helps them generate insights from it.
- Storing the data enables full data access that can be directly used by Business Intelligence (BI) tools to pull data whenever you need.
- The data coming into the data lake supports fast query processing and help you perform ad hoc analytical queries.
How to enhance data lake security?
The primary goal is to enhance data lake security to prevent access to data by unauthorized users. Here are a few simple ways to improve the data lake security:
- Implement network-level security controls access to data via network security policies like firewalls and IP address ranges.
- Grant access control permits to all authorized access.
- Encrypt and decrypt data stored in the data lake during the reading time.