Data Lake Architecture: 10 Critical Aspects

What is a Data Lake Architecture? It is the product of planning and designing scalable storage to handle a growing amount of data while providing faster insights (Data Lake).

Data is a precious asset in every organization. Today, companies are generating a lot of data from clients, operations, and processes. This data is a rich source of information and it can be a game-changer for your organization. 

Research has shown that organizations that use data for decision-making can gain benefits like reaching out to new customers and increasing customer retention rates. However, due to the huge volumes of data that are generated, there is a need for a means to store such data. That’s why you need a Data Lake. 

It helps you store all types of data, whether Structured, Semi-Structured, or Unstructured. You can pull data from multiple data sources into a Data Lake. Before creating a Data Lake for your organization, it’s good for you to understand its architecture. This article introduces the concept of Data Lake Architecture, why you need it, and some of the critical differences between Data Lakes and Data Warehouses. You will also take a dive into Data Lake security and its benefits and challenges.

Table of Contents

• What is Data Lakes?
• Difference between Data Lakes and Data Warehouses
• On-Premise Data Lakes vs Cloud Data Lakes
• Why Build a Data Lake?
• Key Components of Data Lake Architecture
• Understanding the Data Lake Architecture
  • Data Lake Architecture: Ingestion Layer
  • Data Lake Architecture: Distillation Layer
  • Data Lake Architecture: Processing Layer
  • Data Lake Architecture: Insights Layer
  • Data Lake Architecture: Unified Operations Layer
• Best Practices for Data Lake Architecture
• Enhancing the Data Lake Security
• Benefits of Data Lakes
• Challenges of Data Lakes
• Conclusion

What is Data Lakes?

A Data Lake is a data repository for storing large amounts of Structured, Semi-Structured, and Unstructured data. It is a repository for storing all types of data in its native format without fixed limits on account size or file. Data Lake stores a high data quantity to increase native integration and analytic performance. 

You can imagine a Data Lake as a big container that is similar to a real lake and river. Just as a real lake has many rivers flowing in, a Data Lake has machine-to-machine, Structured, Semi-Structured, and Unstructured logs flowing through in real-time. The Data Lake democratizes data and provides a cost-effective way of storing all organization data for later processing. 

It means you can store data as-is in a Data Lake, without having to structure it first, and perform different types of analytics, from visualizations to dashboards to Big Data Processing, Machine Learning, and Real-Time Analytics. 

Difference between Data Lakes and Data Warehouses

Now that you have a basic understanding of Data Lake, let’s understand another term – Data Warehouse. Data Lakes are often confused with Data Warehouses, hence it’s important to draw a line between these two storage techniques to take full advantage of them.

A Data Warehouse is a repository that exclusively keeps pre-processed data from a Data Lake or many databases. ETL (Extract, Transform, and Load) operations are used to arrange data in multi-dimensional structures so that Analytics workflows using Data Warehouses can be accelerated. Business Intelligence specialists and Data Analysts can generate reports and develop dashboards using the data housed in a Data Warehouse.

Data Warehouses store data in a hierarchical format using files and folders. This is not the case with a Data Lake as Data Lake Architecture is a flat architecture. In a Data Lake, every data element is identified by a unique identifier and a set of metadata information.

 The below table highlights the critical differences between Data Lake and Data Warehouse:

You can read Data Warehouse vs Data Lake: 8 Critical Differences to understand in detail when businesses prefer to use Data Lake or Data Warehouse.

On-Premise Data Lakes vs Cloud Data Lakes

Traditional Data Lakes were designed for On-Premises deployments, but the initial generation of Cloud Data Lakes, such as Hadoop, was built for On-Premises deployments as well. Traditional architectures were developed long before the Cloud became a viable stand-alone alternative, and thus failed to achieve the Cloud’s full potential.

Businesses looking for scalable, low-cost data stores were supported by early Data Lake technologies. These On-Premise Data Lakes enabled analysis, which resulted in more informed business decisions. Organizations discovered that On-Premise Data Lake solutions were unsustainable as the volume and relevance of their Big Data systems grew. Traditional On-Premise Data Lakes often fail due to inherent complexity, poor performance, and a lack of control, among other factors.

Since most data is now stored in the Cloud, it makes sense to incorporate it there as well. As a result, several companies began putting together disorganized Data Lakes in Cloud-based object storage, accessible via SQL abstraction layers that need specialized integration and ongoing monitoring. Although a Cloud object store reduces security and hardware management costs, its ad hoc design is frequently slower and necessitates a great deal of manual performance tweaking. As a result, analytics performance is subpar.

Businesses are now relying on Cloud Data Lakes to weave these disparate threads of data into a unified whole. They can acquire, store, and analyze data in modern Cloud Data Lakes to find trends and patterns. Today’s Data Lakes frequently have a Cloud-based Analytics layer that optimizes query performance against data in a Data Warehouse or an external object-store. This allows for more efficient Analytics to delve deeper and quicker into an organization’s diverse data types and formats.

Why Build a Data Lake?

Data Lake provides a large pool of storage to store data from data sources. 4 reasons why build a Data Lake are listed below:

1) Unifying

The company’s data resides in multiple platforms that are used daily. The data can be in ERP systems, CRM platforms, Marketing apps, etc. It helps companies organize the data in their respective platforms. But this is not always the case, when it comes to analyzing all the funnel and attribution data, you need all data together in one place.

Data Lake is a perfect solution to accumulate all the data from distinct data sources in one place. The Data Lake Architecture makes it easier for companies to get a holistic view of data and generate insights from it.

2) Full Query Access

Most enterprise platforms that businesses use to run their daily tasks provide transactional API access to the data. These APIs are not designed to support Reporting tools requirements which end up with limited access to data. Storing data in Data Lakes allows full access to data that can be directly used by BI tools to pull data whenever needed.

ELT process is a flexible, reliable, and fast way to load data into Data Lake and then use it with other tools. 

3) Performance 

Many times data sources are the production systems that don’t provide faster query processing. It can affect the performance of the application that it is powering. Data aggregation requires faster query speed and Transactional Databases are not considered an optimal solution for this. 

Data Lake Architecture supports fast query processing. It enables users to perform ad hoc analytical queries independent of the production environment. Data Lake provides faster querying and makes it easier to scale up and down.

4) Progress

Getting data in one place is a necessary step before progressing to other stages because loading data from one source makes it easier to work with BI tools. Data Lake helps you make data cleaner and error-free data that has less repetition.

Key Components of Data Lake Architecture

Data Lakes allow organizations to save a lot of work and time that is usually invested in creating a data structure. This enables fast ingestion and data storage. Here are a few key components of a robust and effective Data Lake Architectural model:

• Governance: This is vital in order to measure performance and improve Data Lake through monitoring and supervising operations.
• Security: This is a key component to keep in mind during the initial phase of architecture. This is different from the security measures deployed for Relational Databases.
• Metadata: Metadata means data that is referring to other data. For e.g. reload intervals, schemas, etc.
• Stewardship: Depending on the organization, this role can be assigned to either the owners or a specialized team.
• Monitoring & ELT Processes: A tool is required to organize the flow of data that is moving from the Raw layer through the Cleansed layer to the Sandbox and Application Layer since one might need to apply transformations to the data.

Understanding the Data Lake Architecture

A typical Data Lake consists of 5 layers:

• Data Lake Architecture: Ingestion layer
• Data Lake Architecture: Distillation layer 
• Data Lake Architecture: Processing layer 
• Data Lake Architecture: Insights layer
• Data Lake Architecture: Unified Operations layer 

1) Data Lake Architecture: Ingestion Layer

The purpose of the Ingestion Layer of the Data Lake Architecture is to ingest Raw Data into the Data Lake. There is no data modification in this layer. 

The layer can ingest Raw Data in real-time or in batches, which is in turn organized into a logical folder structure. The Ingestion Layer can pull data from different external sources like social media platforms, wearable devices, IoT devices, and Data Streaming devices. 

The good thing with this layer is that it can quickly ingest any type of data including:

• Video streams from security cameras. 
• Real-time data from health monitoring devices. 
• All kinds of telemetry data. 
• Photographs, videos, and geolocation data from mobile devices. 

2) Data Lake Architecture: Distillation Layer

The purpose of the Distillation Layer of the Data Lake Architecture is to convert the data stored in the Ingestion Layer in a Structured format for analytics. 

It interprets Raw Data and transforms it into Structured Data sets that are stored in files and tables. The data is denormalized, cleansed, and derived at this stage, and it becomes uniform in terms of format, encoding, and data type. 

3) Data Lake Architecture: Processing Layer

This layer of the Data Lake Architecture executes user queries and advanced analytical tools on the Structured Data. 

The processes can be run in batch, in real-time, or interactively.  It is the layer that implements the business logic and analytical applications consume the data. It is also known as the Trusted, Gold, or Production-Ready Layer. 

4) Data Lake Architecture: Insights Layer

This layer of the Data Lake Architecture acts as the query interface, or the output interface, of the Data Lake. It uses SQL and NoSQL queries to request or fetch data from the Data Lake. The queries are normally executed by company users who need access to the data. Once the data is fetched from the Data Lake, it is the same layer that displays it to the user for viewing. 

The output from queries is normally in the form of reports and dashboards, which make it easy for users to extract insights from the underlying data. 

5) Data Lake Architecture: Unified Operations Layer

This layer of the Data Lake Architecture monitors and manages the system using workflow management, proficiency management, and auditing. 

Some Data Lakes implement a Sandbox Layer to provide data scientists and advanced analysts with a place for data exploration. 

Best Practices for Data Lake Architecture

For effective digital transformation, knowing authentic and accurate data sources in an organization is important in order to capitalize on ever-increasing volumes of data. This will help an enterprise generate new insights that will propel growth. An effective Data Lake should pass the following checklist of criteria:

• Reduced effort to ingest data.
• Cost-efficient storage of large volumes of data.
• Facilitation of advanced analytics scenarios.
• Ability to work on all data types, while functioning at a high velocity and dabbling with voluminous data.

These are a few of the best practices to build robust Data Lakes:

• Develop Data Governance, Privacy, and Security: Data governance and metadata management are essential for the effective maintenance of a robust Data Lake strategy. A well-maintained Data Lake consists of data that is clean, reliable, secure, and easily accessible. Due to high-quality data residing at a Data Lake, it can easily be utilized for various purposes by business users. Hence, it is imperative that the responsibility for data be built.
• Leverage Automation and AI: Data acquisition and transformation process should be automated due to the speed and diversity of the data coming into the Data Lake. Organizations can leverage top-notch data storage, data integration, and analytical techniques to classify, analyze and learn from the data swiftly with better accuracy.
• Integrate DevOps: DevOps processes are responsible for building and maintaining a reliable Data Lake. Clear guidelines need to be established regarding where and how is the data collected. One needs to ensure that these standards are strictly adhered to while evaluating if the sources are trustworthy or not and taking essential preventive measures to ensure reliability.

Enhancing the Data Lake Security

The major goal of Data Lake security is to prevent access to the data by unauthorized users. To keep your Data Lake secure, security must be implemented at every layer of the Data Lake. At the same time, it should be easy for users to access the data through GUI and dashboards that are easy to navigate through. Some of the common features of Data Lake security are Accounting, Authentication, Authorization, and Data Protection. 

These are accomplished as follows:

• Implementing network-level security controls access to data via network security policies like IP address ranges and firewalls. 
• Granting access control permits to authorized users. You can also set different user permissions and roles. 
• All data stored in a Data Lake should be encrypted and only decrypted during reading time. End-to-end encryption should be applied when the data is in transit. 

Benefits of Data Lakes

As data volumes continue to grow, Data Lake architecture has improved in recent times to better suit the expectations of increasingly data-driven companies. Let’s look at some of the incredible advantages that Data Lakes provide:

• High Scalability: Scalability refers to a data system’s, network’s, or process’s capacity to handle increasing amounts of data, as well as its ability to expand to accommodate that growth. When Data Lakes’ scalability is taken into account, it is quite affordable when compared to a standard Data Warehouse.
• AS-IS Data Format: At the moment of data input or ingress, data in older data systems are represented as cubes. However, in the Data Lake, data modeling is not required at the time of input; instead, it may be done at the time of consumption. It provides unrivaled versatility in terms of asking any business or domain query and receiving insights and intelligence responses.
• Supports Many Languages: Traditional data-warehouse technology primarily supports SQL, that’s fine for basic analytics, but we need other ways to examine data for sophisticated use cases. For analysis, a Data Lake gives a variety of tools and language support. It has Hive/Impala/Hawq, which not only supports SQL but also has advanced features. PIG, for example, may be used to analyze the information in a flow, while Spark MLlib can be used to do Machine Learning.
• Advanced Analytics: Unlike a traditional Data Warehouse, the Data Lake excels at identifying objects of interest that will enable real-time decision Analytics by combining massive amounts of coherent data with Deep Learning algorithms.

Challenges of Data Lakes

A Data Lake offers some key advantages as discussed above in the form of faster query results at low-cost storage, and provides support for Structured, Unstructured, and Semi-Structured Data but it is not without its challenges as well.

One of the main challenges of a Data Lake architecture is that raw data is stored with no management over what is stored. To make data useable, a Data Lake must have specified processes for cataloging and securing data. Data cannot be found or trusted without these elements, resulting in a “Data Swamp“. Hence, Data Lakes must include governance, semantic consistency, and access restrictions in order to meet the demands of a larger audience.

The following are the challenges associated with the design, development, and use of Data Lakes:

• Immature Data Security and Governance: Data Lake solutions are terrific at storing data, but not so much at safeguarding it or enforcing data governance requirements. You’ll have to add security and governance to the mix. This means more wasted time, money, and management headaches.
• Lack of Skill Set: The process requires the use of new tools and services that should be understood. The company may have to recruit new members or do internal professional development. 
• Unstructured Data: In most cases, Data Lakes store Unstructured data. When users attempt to work with such data, it raises more questions than answers. 
• Lack of Tools: It may be a challenge to get a tool or tools to help you pull data from multiple data sources into your Data Lake, especially when there is a need to do it in real-time. 
• Rise in Management Complexity: Data Lakes are difficult to maintain even for experienced engineers. Whether you’re using a vanilla open-source Data Lake platform or a managed service, ensuring that your host infrastructure has the capacity for the Data Lake to keep expanding, dealing with duplicate data, protecting all of the data, and so on are all difficult tasks. As a result, strong data management techniques are required in enterprises. Otherwise, the Data Lake might devolve into a data swamp, rendering it useless.

Conclusion

This article introduced you to Data Lake and explained its importance. You also came across the Data Lake vs Data Warehouse comparison. In addition, you explored the On-Premise and Cloud Data Lakes. It further discussed the basics of a Data Lake, the different layers of a Data Lake, and various important concepts like Data Lake Security, benefits, and challenges associated with Data Lakes.

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Now, to perform Data Analytics on your data stored in Data Lake, you first need to export this data to a Data Warehouse. This will require you to custom code complex scripts to develop the ETL processes. Hevo Data can automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Customer Management, etc. This platform allows you to transfer data from 100+ multiple sources like Data Lakes to Cloud-based Data Warehouses like Amazon Redshift, Snowflake, Google BigQuery, etc. It will provide you with a hassle-free experience and make your work life much easier.

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