4 Key Components of Data Lake Architecture

data lake architecture

4 Key Components of Data Lake Architecture

Data lakes provide a centralized location for massive volumes of different kinds of data. They act as a scalable infrastructure to store, process, and analyze these large swaths of information. Unlike a data warehouse, which organizes data into files and folders, data lakes are unstructured, storing data in its raw format from sources like analytics systems, dashboards, social media, mobile apps, and more.

Data lakes are powerful because they allow teams to use that data to perform things like machine learning, predictive analytics, data discovery, job scheduling, data management, batch processing, big data, and more. They retrieve data in all its forms, process the data to produce applicable information, analyze it, and store those results in a data storage system. All the underlying raw data remains, the data lake just draws on the insights of that data to create the bigger picture.

Many businesses choose to implement data lakes to foster deeper analytical options for their organization. Because a data lake can store so many different kinds of data, it’s a logical choice for eliminating data silos, facilitating more advanced analytics, and creating a centralized view of data that benefits everyone.

If you’re interested in expanding your data power, there are four key components you’ll need for your data lake architecture.

 

#1: A Security and Governance Plan

Ever heard of a data swamp? Without data governance and security, your sterling data lake can become one. Data security and governance policies ensure the data lake will be clean, secure, and efficient to search within. Security and governance policies should answer questions like the ones below. They are marked with an S for security or a G for governance to differentiate between their functions.

  • What’s the plan for monitoring the operations of the data within the data lake? (G)

  • What about managing the metadata? (G)

  • Which users can access the data lake? How are they being authenticated? (S)

  • What are the data encryption policies? (S)

  • What authentication methods will be used to keep data secure? (S)

  • What policies will we have for semantic consistency of the data that’s entered? (G)

  • Are we recording user transaction audit logs so we can trace anomalies? (G)

  • How often will we perform a data audit and what will that include? (G)

 

Data security measures should include role-based access options, multi-factor authentication methods, and data protection rules. Governance focuses on the entire ingestion/cataloguing/integration of the data and the policies that will govern these processes. One aspect of governance that should not be overlooked are semantic consistencies. Without standardized naming conventions, data will not be easily found, therefore making it impossible to report on. This is why some sort of semantic consistency (ex: metadata tags) must be established as part of the governance policy.

With established policies in place, a designated data steward can enforce the governance and security and make determinations of when and whether to allow exceptions. This structured approach reduces the risk of data theft and also maintains a cleaner, more useful data lake.  

 

#2: Automated Data Ingestion

Just as a real lake would be filled through various streams and rivers, a data lake is bringing in data from many different sources, and in various formats: structured data, unstructured data, weblogs, IoT and sensor data, etc.  It’s also coming in 24/7, which makes it much harder to manage manually. To reduce latencies and scale large volumes of data faster, automate your data ingestion.

A comprehensive automated data ingestion tool should do more than just bring in the data, though. If a failure occurs, your teams will need to know about it. Make sure the automated solution can perform quality checks on incoming data, automate the metadata application, and manage the data lifecycle in addition to the basic role of ingesting data. These tasks will help your team identify the cause of any failures sooner and with less upfront legwork.

A scalable ingestion tool should be flexible enough to run in batch, one-time, or real-time. It should easily support current data types as well as new data sources that you may want to add later. Be aware of this when choosing your ingestion tool and creating your automation strategy so you can maximize the efficiency of data loads.  

 

#3: Enabled Analytics

Data lakes are often part of a larger data ecosystem, working together with other solutions to foster things like a digital supply chain and omnichannel marketing. Once data is ingested into the data lake in its raw form, data analytics and machine learning tools should engage to pull the insights from that data and move organized information to a data warehouse. Serverless computing options are the most common tools to run these analytical algorithms because they can scale, do not require server management, and are cost effective. Google, Microsoft Azure, and AWS are three of the big names that provide these serverless computing architectures.

 

#4: Scalable Data Storage

The whole point of a data lake is to make massive quantities data from different sources available to end users like business analysts, data engineers, executives, and others. The storage in the data lake should be robust and scalable to achieve this. It should also support the encryption measures you require via your data security policies and be able to compress data for faster ingestion. These features will make the data storage more efficient, and therefore more cost-effective in the long term.