What is a Data Lake? Definition, Architecture, Tools, and Applications

A data lake is a system where one can retain all types of information in object storage units or “blobs” to power analytics, machine learning, and other data uses in a different ex-situ location. This article explains the architecture of data lakes, the top tools to use, and critical applications in enterprise IT.

What Is a Data Lake?

A data lake is defined as a data system designed primarily for unstructured data, where one can store information in object storage units or “blobs” to power analytics, machine learning, and other data uses in a different ex-situ location.

To get a competitive edge in the market, organizations have collected a lot of data from their consumers and key stakeholders. Demand for space for storage of this data has therefore been on the rise, which data lakes provide.

Data lakes provide a repository for storing vast amounts of data in structured, semi-structured, and unstructured data forms. They allow for secure and efficient raw data storage without fixed limitations for future analytical performances.

Unlike data warehouses, data stored in data lakes don’t have to be pre-processed. Data lakes also provide a cost-effective way of storing data. Data stored in warehouses are used chiefly by business analysts, while data lakes can be used by data scientists, data developers, and business analysts.

Modern data lakes provide low-cost data stores that can be scalable by storing their data in the clouds, unlike traditional ones, which use on-premise storage facilities. Modern data lakes frequently have a cloud-based analytics layer that optimizes query performances against data in a data warehouse. This ensures more efficient analytics.

Data lakes provide solutions to organizations looking to accumulate all the data from distinct data sources in one place to generate insights from it. Data lakes allow business intelligence (BI) tools to directly pull data when needed, making it a complementary analytical support tool. They also support faster querying as data scientists can perform analytical queries independent of the production environments. Data lakes are also highly scalable and support many languages.

One of the main challenges that data lakes present is that since the raw data is stored “as is”, they must have specific processes for cataloging and securing data failure to which queries may not be fulfilled. There’s also a general lack of skill set required to use tools in data lakes that may necessitate new recruitments or internal professional developments leading to increased costs.

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Data Lake Architecture

Organizations generate large volumes of data from their consumers, operations, and processes. Data scientists can analyze the data generated to improve customer retention rates and reach out to new ones. Given how “big” that data can get, data lakes are critical as they provide a platform where data can be stored efficiently, safely, and easily retrieved for analysis. With more technological advancements, the importance and demand for storage of this data increases. That is where data lake architecture comes in. 

Data lake architecture cooperates with both the planning and designing of scalable storage to handle the increasing demand for data storage while providing faster insights. The data lake architectural model is made up of the following key components that allow it to be effective and robust:

1. Security

In the era of increasing cyber threats, data lakes must have good security to prevent data breaches that may result in data theft. Companies should put security factors such as authorization, role-based access, and multi-factor authentication in place.

2. Governance

The sanctity of the whole process of data ingestion, preparation, cataloging, integration, and query acceleration should be made streamlined. This will ensure that improvements can be made on the data lakes when needed.

3. Monitoring and ELT processes

One should put organization tools in place to organize data flow from the raw layer through the cleansed layer to the sandbox and application layer in case any data transformation is required. Supervision is assigned to the organization, owners, or anyone else in charge of the data and data lakes.

To support these components, the data lake architecture will consist of five layers:

• Ingestion layer: The purpose of the ingestion layer is to ingest raw data into the data lake in real-time or in batches. The data is then organized into a logical folder structure. Internet of Things (IoT) devices, data streaming devices, telemetry data, geolocation data from mobile devices, and social media platforms can act as external data sources. In this layer, data is not modified.
• Distillation layer: In this layer, the data stored in the ingestion layer is converted into a structured format in readiness for analysis. The structured data is then stored in files and tables. The data is made uniform in terms of encoding, data type, and formatting after it is denormalized, cleansed, and derived.
• Processing layer: This layer executes queries by data scientists and provides advanced analytical tools for structured data. The requests can be run in batches, real-time, or interactively as the need arises.
• Insights layer: The purpose of this layer is to act as the query interface of the data lake. It uses structured query language (SQL) and NoSQL queries to request data from the data lake. It allows the display of data to the data scientists who request it.
• Unified operations layer: The purpose of this layer is to monitor and manage the system using workflow management, proficient management, and auditing to streamline the process.  

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Top 8 Data Lake Tools

As per a 2022 Mordor Intelligence report, the global data lake solution market is expected to reach $17.60 billion by 2026. This provides enterprises with powerful tools for data lake management, such as:

1. Azure Data Lake Storage

Azure Data Lake Storage was developed and released by Microsoft in 2016. It’s based on COSMOS, which features a SQL-like query engine known as SCOPE. It stores and processes data for applications such as Azure, AdCenter, Skype, and Windows Live. Azure allows for cloud storage of structured, semi-structured, or unstructured data produced from applications such as social media, videos, relational data, and sensors.

It provides a single storage platform for ingestion, processing, and visualizations that support the most common analytics frameworks. It also provides a cost-effective way to store data in a secure location. Data is encrypted at rest, and authentication can be done using Azure Active Directory and role-based access control.

2. AWS Lake Formation

Amazon Web Services (AWS) Lake Formation is a product of Amazon that allows for a centralized, curated, and secure repository to store data. It allows for data to be held in its raw form or pre-processed data that is ready for analysis.

AWS provides a simplified approach where users must define their data sources and what access and security policies to apply. Then, AWS helps collect and catalog data from databases and object storage and moves it into their Amazon Simple Storage Service (S3).

AWS provides a simplified security management system that allows for a single place to define and enforce access controls for all users needing data access. These policies are consistently implemented to reduce the need for manual input. Consequently, data scientists and analysts can easily access the right data asset and thus improve productivity.

3. Alder Lake P

Alder Lake P is a product of Intel and was officially released in November 2021. It is a codename for the 12th generation of Intel Core processors based on a hybrid architecture that utilizes Golden Cove High-performance cores (P- cores) and Gracemont power-efficient cores (E-cores).

Alder Lake P is an innovative solution that features up to 14 cores and 20 threads built for multitasking. This chip provides enhanced graphics that allow for eye-catching visuals with up to 96 graphics execution units. They also deliver up to 2.47x times faster graphics than their preceding chip. Alder Lake P can be used in the video and security sector by providing quality videos and catering to artificial intelligence-based video analytics.

4. Snowflake Inc.

Snowflake Inc. provides cloud-based data lakes using a new SQL database engine with unique cloud-based architecture. It was publicly launched in October 2014 and allows clients to store their data in the cloud and ready-to-use tools for data analysis. 

In 2014 it began running on Amazon S3, on Microsoft Azure in 2018, and on the Google Cloud Platform in 2019. Snowflake ensures data is safely secure by encrypting data on transit and at rest. It also allows for secure data sharing and integration with external tools.

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5. Google BigLake

Google BigLake is a product of Google that was released in April 2022. It is a storage engine that allows organizations to analyze the data in their data warehouses and data lakes. It takes advantage of Google’s experience managing its BigQuery data warehouse and extends it to data lakes on Google Cloud Storage. Users can query the underlying data stores through a single system without data duplication. This reduces costs and minimizes inefficiencies.

Google BigLake allows for fine-grained access control eliminating the need to grant file-level access to end users. It also provides multi-compute analytics that ensures a single copy of data is available across Google Cloud and open-source engines.  

6. Qubole

Qubole is a simple cloud-based and secure data lake platform for streaming, ad hoc analytics, and machine learning that Idera acquired. It was founded in October 2011. It manages Hadoop infrastructure and allows users to prepare, integrate and analyze big data. Users can use both structured and unstructured data. 

Qubole’s user interface allows data analysis without prior knowledge of Hadoop systems management. It provides a platform where data from multiple sources can be integrated and allows automatic scaling based on workload.

7. Databricks Lakehouse

Databricks Lakehouse provides a single platform where users can unify their data warehousing and AI use. Combining the best elements of both lakes and warehouses eliminates data silos that traditionally separate and complicate data engineering, BI, and machine learning. It allows multi-cloud access to secure data and easy data sharing.

8. Teradata Vantage

Teradata Vantage is a multi-cloud data platform that unifies data lakes, data warehouses, new data sources, types, and analytics. It allows users to secure role-based queries and scale up and down depending on the workload. It’s a cost-effective platform that integrates any type of data from sources within the organization to provide a single source of truth to allow practical data analysis.

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Data Lake Applications 

The applications of data lake technology span nearly every sector, from healthcare to manufacturing and from HR to enterprise resourcing planning. A data lake can be used in the following ways:

1. Archiving historical data

In specific scenarios, one may collect data with no apparent future purpose. Several organizations, such as hospitals and telecommunication companies, hoard data without any plan to mobilize it. While getting rid of it could solve their storage problems, it’s not uncommon for various issues to arise in the future, and the data may be required. 

This is where data lakes come in handy. They enable vast data streams to be stored “as is” and accessed on demand. The data lake architecture ensures that companies can store massive streams of historical data efficiently and safely as it awaits demand.

2. Supporting experimental analysis

A data lake provides a platform where data scientists can access raw data and use it for experimental analysis. A data lake ensures that data scientists can query data easily without having to relocate it first. This is because data lakes employ an “ELT” strategy that is ‘extract’ ‘load’ and then ‘transform’ if necessary. Data is therefore stored with relative ease and can easily be extracted if a need arises. 

Data scientists thus can access and perform several different experimental analyses with relative ease, and in case the data gets corrupted, one can quickly access the original data. Businesses can thus define the value of the data they collect and use it to improve customer relations.

3. Providing advanced analytics support

In this era of technological advancement, several business enterprises collect data from their consumers and other stakeholders. The difference between failure and success can lie in how business enterprises use consumer data. Data scientists can provide analytical support by analyzing the data collected and giving advice on consumer behavior. In this way, a data lake is helpful for data scientists to provision and experiment with data.

4. Storing end-to-end organizational data

A data lake provides a solution for organizations needing a place to store all the data they collect. The architecture provided by data lakes ensures that massive data streams can be collected and stored efficiently and safely. Organizations can achieve a single repository for all types of data they collect to support any future data analysis that may prove to be of value. Data lakes, therefore, prove to be a must-have solution for different types of organizations and business enterprises.

5. Ingesting semi-structured and unstructured sources

Data lakes allow for the storage of data “as is”. This feature allows data to be stored in structured or unstructured forms. Structured data can be stored as a single repository that one can easily access. 

Businesses can also store vast streams of unstructured data according to the preferences of different organizations. This ability helps hold the Internet of Things (IoT) which traditionally has always been a challenge to store and support in need analysis. Big data such as logs, equipment readings, and streaming data can also be stored in data lakes.

6. Powering Lambda architecture

Lambda architecture provides a hybrid approach by enabling access to batch-processing and stream-processing to support big data analysis. Using data lakes, one can solve the problem of computing arbitrary functions. Lambda structure comprises the batch layer, the speed layer, and the serving layer. Data lakes provide the platform upon which one can build this architecture. The Lambda architecture offers real-time analytics and enables the drive to mitigate latencies of complex data processing algorithms like MapReduce.

7. Preparing for data warehousing

Data lakes can sometimes be used as staging zones for data warehouses. Enterprises can store structured data from operational databases as it waits for data scientists to access and extract insights. Data warehouses allow for efficient data storage by minimizing input and output and delivering queries quickly as needed.

8. Augmenting warehousing capabilities

Sometimes data lakes may contain vast data streams that are not easily stored in data warehouses or frequently queried by data scientists. Data lakes separate from the data warehouses to the required parties via data virtualization layers if accessed via federated queries.

9. Assisting in distributed data processing

Organizations with massive data sometimes opt to collect different types of data and store it for analysis. When this data is transferred to data lakes, the data lakes become heterogeneous. The architecture of data lakes allows efficient storage of data. It also provides for a unified view of data via data virtualization. Data lakes allow for queries to be made easily, making a streamlined process of storing and retrieving data. This process makes data lakes a must-have solution for data scientists.

10. Providing application support

Data analytics is not the only purpose of the information stored in data lakes. Sometimes, data lakes may serve as data sources for a front-end application. Data lakes can store big data in a secure, efficient, and easily accessible format and thus could make excellent data sources.

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Takeaway 

As the use of big data grows, on-premise and cloud-based data lakes are becoming an enterprise staple. As per BI-Survey.com’s 2020 Hadoop and Data Lakes Report, nearly half of users worldwide benefit from data lakes, and almost 1 in 3 enterprises agree it is the central point of all their data. However, one should keep in mind that lakes require regular upkeep so that they do not turn into a “data swamp” and the addition of new technologies can help extract more value out of idle information. 

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