Things to Learn About Data Lake | What is a Data Lake?

ABCAdda | Updated Jun 07, 2022

Data lake technology is new! Data lake that supports advanced analytics or are unfamiliar with some of the key concepts related to the data lake; this blog post is for you. It is intended as a short textbook for both technical and non-technical audiences.

What is a cloud data lake, and how does datalake work?

A data lake is a centralized system or data warehouse that allows you to store all your structured, semi-structured, unstructured, and binary data in natural/native format/raw formats.

Structured data can contain RDBMS tables; semi-structured data include CSV files, XML files, log files, JSON, etc.; unstructured data can include PDF files, text files, emails, etc.; binary data can include audio, video, and image files.

It follows a flat memory architecture. Data is usually stored in the form of objects or files.

With a data lake, you can keep your entire company in one place without organizing the data first. You can run various types of analytics directly on it, including machine learning, real-time analytics, local traffic, real-time traffic, dashboards, and visualizations.

He keeps all the data in its original form and assumes that the analysis will be carried out later upon request.

Cloud data lake definition

A data lake is a repository that stores large amounts of raw data in its original format while not being needed for analytical applications.

Why Data Lake | datalake?

The main purpose of building a data lake is to provide data scientists with a view of unclean data.

The reasons for using Data Lake are:

  • With the advent of storage engines like Hadoop, storing various kinds of information has become easy. There is no need to model enterprise-wide data with data in the lake.
  • As the volume of data, data quality, and metadata increases, analysis quality also increases.
  • Data Lake offers business agility.
  • Can use Machine learning and artificial intelligence to make profitable predictions.
  • This offers implementing organizations a competitive advantage.
  • No silo data structure. Data Lake provides a 360-degree view of customers and makes analytics more powerful.

Data Lakes Storage Technology | google data lake

There are various technologies for storing data lakes. Popular solutions for storing data lakes are:

  • Inherited HDFS (Hadoop Distributed File System)
  • Amazon S3
  • Azure blob storage
  • Azure Data Lake Storage (ADLS)

These are just a few examples, but many other on-premises and cloud solutions exist. They work the same way, using a distributed system where data is spread across multiple inexpensive hosts or cloud instances. Data is usually stored in multiple locations at once to provide backup if something goes wrong.

Data lakes file and table formats

Traditional databases have to store data in a very specific and organized way, but data in the lake can easily store any type of data—whether fully organized when uploaded or not fully structured.

Data lakes can store a variety of file formats. Common file formats for data storage are:

  • Comma Separated Values ??or CSV
  • Object notation in JavaScript or JSON
  • Open source optimized for applications like Apache Parquet

A table format is a metadata construct that makes it easy to interact with the files in a table.

Data lakes are just as useful as their metadata. A table format is a metadata construct that helps you understand what data you have in your data set and makes it easier to use. Common table formats are:

  • Apache Iceberg (open source)
  • Delta Lake (Databricks)

Meta shop stores metadata for all the tables in your data lake and how they are structured, essentially acting as a catalog for everything in your lake. Metastases in the data lake include:

  • Arctic Dremio
  • Data lakes AWS glue
  • Nest megastore

Datalake architecture

The following are the important layers in a data lake architecture:

what is the Data lake architecture? The following are the important layers in a data lake architecture:

  • Absorption Field: The field on the left represents the data source. Data can be loaded into batch datasets or in real-time.
  • Insight Level: The level on the right represents the research field where insights from the system are used. Can use SQL, NoSQL queries, or even Excel for data analysis.
  • HDFS: HDFS is a cost-effective solution for structured and unstructured data. This is the landing area for any data not active in the system.
  • Degree distillation: takes data from mature storage and converts it into structured data for easier analysis.
  • Processing layer: This runs analytic algorithms, and user queries on various interactive structured data generated in real-time analytics for easier analysis.
  • Unified operations layer: This manages system administration and monitoring. This includes exam and competency management, data management, and workflow management.

Key concepts for data lakes

The following are key data lake concepts that must be understood to understand data lake architecture fully:

The following are key data lake concepts that must be understood to understand data lake architecture fully:

Data collection: Data ingestion allows the connector to receive data from different sources and load it into a data lake. Supported data reception:

  • Everything related to all structured, semi-structured, and unstructured data.
  • Multiple takeovers as a batch, real-time, single load.
  • Many data sources such as databases, web servers, email, IoT, and FTP.

Data warehouse: Data storage must be scalable, provide cost-effective storage, and provide quick access to research data. It must support different data formats.

Data management: Data management is managing the availability, usability, security, and integrity of data used in an organization.

Security: Security must be implemented at every layer of the data set. It starts with storage, excavation, and consumption. The main requirement is to prevent access for unauthorized users. It should support various data access tools with an easy-to-navigate GUI and dashboard.

Authentication, billing, authorization, and privacy are key features of data lake security.

Data quality: Data quality is a key component of a data lake architecture. The data is used for accurate business value. Extracting insights from poor quality data results in poor quality.

Data detection: Data discovery is another important step before starting any data preparation or analysis. In this phase, labeling techniques are used to express understanding of the data by organizing and interpreting the data absorbed into the data lake.

Data validation: The two main tasks for data review are tracking changes in the main data set.

  • Track changes to key elements of the record
  • Tracks how/when/and who changed in these elements.

Data review helps with risk assessment and compliance.

Data origin: This component relates to the origin of the data. It’s mostly about where he goes from time to time and what happens to him. This makes it easy to correct errors in analyzing data from source to destination.

Data research: This is the first stage of data analysis. This helps to identify the exact data set that is important before starting data exploration.

All of the given components must work together to play an important role in building the data pool for the environment to develop and explore easily.

Data Lake maturity level: The definition of a data lake maturity level differs in different textbooks. However, the essence remains the same. After maturity, the level is determined from a layman’s point of view.

Data Lake Maturity Stages

  • Step 1: Process and record scale data: This first phase of data maturity involves increasing the ability to transform and analyze data. Here, business user owners need to find tools according to their expertise to get more data and build analytics applications.
  • Stage 2: Build analytical muscle: This is the second stage, which involves increasing the ability to transform and analyze data. At this stage, companies use the tools that best suit their expertise. They started collecting more data and building mobile apps. Possibility of shared enterprise data warehouse and data lake.
  • Stage 3: EDW and Data Lake working together: This step is all about getting data and analysis into the hands of as many people as possible. At this point, the enterprise data lake and data warehouse begin to work together. Both play a role in the analysis.
  • Stage4: Factory capacity on the lake: Business user functions are added to the data set in this phase of data set maturity. Introduction to information management, information lifecycle management functions, and metadata management. However, very few organizations can reach this level of maturity, but this number will increase in the future.

Best practices for data lake implementation:

Architectural components, interactions, and identified products must support native format data types.

  • Data lake design should be based on what is available, not needed. Schema and data requirements are only specified on the request.
  • Design should be guided by one-way components integrated with the service API.
  • We must independently manage the discovery, reception, storage, management, quality, transformation, and data visualization.
  • Industrial Lake Architecture must be industry-specific. It should ensure that the skills required for that domain are an integral part of the design.
  • Faster adoption of newly discovered data sources is important.
  • Data Lake helps personalized management make the most of it.
  • Data Lake must maintain existing enterprise data lake management techniques and methodologies.

What challenges do data lakes pose?

Despite the business users’ benefits that data lakes offer, implementing and managing them can be a difficult process. These are some of the challenges that data sets pose for businesses:

  • Data swamp: One lake of AWS’s biggest challenges is preventing the data lake from becoming a data swamp. If not properly organized and managed, data pools can become messy data stores. Users may not find what they need, and data managers may lose track of the data stored in the dataset even if more is spilled.
  • Technological advantages: The various technologies used in the data set also make implementation difficult. First, organizations need to find the right technologies to meet their specific data management and analytics needs. You’ll then have to install it, although the increasing use of the cloud has made this step easier.
  • Unforeseen expenses: While the initial technology costs may not be very high, they can change if organizations do not carefully manage their data lake environment. For example, companies may get unexpected charges for cloud-based data sets if they use more than expected. The need to develop data sets to meet load requirements also increases costs.
  • Data management: One of the purposes of a data lake is to store raw data for various analytical purposes. But without effective data lake management, organizations can be affected by data quality, consistency, and reliability issues. This issue can interfere with analytics applications and produce erroneous results that lead to poor decisions for business users.

Difference between data lake vs. data warehouse

People often find it difficult to understand how a lake differs from a data warehouse. They also claim that this is the same as a data warehouse. But that’s not the reality.

The only thing that data pools and data warehouses have in common is that they are both repositories. Relax, they are different. They have different uses and purposes.

The differences between aws data warehouse Vs. Data lake are explained below:


  • Data Lake: Data Lake stores all raw data in it. It can be structured, unstructured, or semi-structured. We may never use some data in the data lake.
  • Data warehouse: The warehouse contains only processed and refined data, i.e., structured data needed to report and resolve specific business users’ issues.


  • Data Lake: Data Lake users are data scientists and data developers.
  • Data warehouse: In general, data warehouse users are business people, operational users, and business analysts.


  • Data Lake: The data lake is accessible, easy, and fast to update because it has no structure.
  • Data warehouse: In a data warehouse, updating data is a more complex and expensive process because the data warehouse is structured by design.

To plan

  • Data Lake: Recording scheme. Designed before DW implementation.
  • Data warehouse: scheme on reading, written during analysis.


  • Data lake: Flat architecture
  • Data warehouse: hierarchical architecture


  • Data lake: The purpose of the raw data stored in the data lake is not fixed or undefined. Sometimes data can flow into a data lake for future use or own the data. Data lakes have data that are less organized and less filtered.
  • Data warehouse: Stored in the data warehouse has a specific and specific purpose. DW organizes and filters data. Therefore, it requires less storage space than the data pool.


  • Data Lake: It can be used for machine learning, data profiling for data discovery, and predictive analytics.
  • Data warehouse: It can be used for business intelligence, visualization, and batch reports.


  • Data Lake: Designed for low-cost storage. Data lake hardware is very different from data warehouse hardware. It uses prebuilt servers in combination with cheap storage. This makes data lakes quite economical and highly scalable down to terabytes and petabytes. This is done to store all the data in a single data lake, so you can always go back in time for analysis. Expensive for large amounts of data.
  • Data warehouses: They have expensive disk space to make them highly productive. Therefore, to save space, the data model is simplified, and only the data necessary for business decisions is stored in the data warehouse.

Support for data types

  • Data Lake: It supports excellent non-traditional data types such as server logs, sensor data, social media activity, text, images, multimedia, etc. All data is stored regardless of its source and structure.
  • Data Warehouse: Generally, a data warehouse consists of data retrieved from transactional systems. Non-traditional data types are not well supported. Storing and using non-traditional data can be expensive and difficult with data warehouses.


  • Data lake: Security is mature as it is a relatively new concept in the data warehouse.
  • Data warehouse: Security is at the “mature” stage.


  • Data lake: Very agile; configure and reconfigure as needed.
  • Data Warehouse: less agile; fixed configuration.

Azure data lake vs. AWS data lake


  • AWS: Release date 2006 Launched 2010 Launched
  • Azure: 31% market share World computer market share 11%.


  • AWS: Zone 61 Availability
  • Azure: Zone 140 Availability Zone

Storage service


  • S3
  • Basket
  • EBS
  • SDS
  • Domain
  • Easy to use
  • SQS
  • CloudFront
  • AWS Import/export

Azure storage

  • Receptacle
  • Azure Drive
  • desk storage
  • Table
  • memory statistics

Database service


  • MySQL
  • fortune-teller
  • dynamo DB


  • MS SQL
  • SQL Sync

Implementation Service


  • Amazon Web Services
  • Amazon Machine Copies (AMI)
  • Traditional implementation model
  • Minor update
  • Elastic bean stem
  • Cloud data lake formation


  • Cspkg (effective zip file)
  • Upload via portal or API via blob repository
  • Updates tailored to the course
  • “Click to enlarge.”
  • More magic

Network service


  • IP / Elastic IP / ELB
  • Virtual Private Cloud
  • 53 street
  • ALL
  • Firewalls are highly configurable


  • Automatic IP assignment
  • load balancer
  • Azure Connection
  • Compensation
  • Endpoints defined in def/cscfg

Hourly rates

  • AWS: Rounded Ordered on request.
  • Azure: On-demand


  • Aws: Adobe Client, Airbnb, Expedia, Yelp, Nokia, Netflix, Novartis.
  • Azure: Pearson, 3M, Towers Watson, NBC, Essar, Serko, etc.

Cloud types

  • AWS: Virtual Private Cloud (VPC) Virtual network
  • Azure: Connection type Direct Connect ExpressRoute

price model

  • AWS:
    • free tier Per hour
    • Free trial per minute
    • No change stops
    • Pay for EBS volumes


  • Free trial period
  • In one minute

Government Cloud

  • AWS: Has advantages over Government Cloud offerings.
  • Azure: Limited scope for government cloud proposals.

Hybrid cloud support

  • AWS: Doesn’t offer the best hybrid cloud data lake support.
  • Azure: Enterprises can integrate on-premises servers with cloud instances with a hybrid cloud.


  • AWS: AWS has a software marketplace with a broad partner
  • ecosystem.
  • Azure: With very few Linux options, Azure doesn’t have a huge ecosystem.

Big Data Support

  • AWS: Ideal for big data.
  • Azure: Standard storage has a lot of big data issues, so you need premium storage.

Big data

  • AWS: A more mature cloud environment for big data.
  • Azure: Immature data Environment.

Machine access

  • AWS: Machines can be accessed separately.
  • Azure: The machines are grouped in the cloud service and correspond to the same domain name with different ports.


  • AWS: The average salary for an AWD engineer is about $141,757 per year for a software architect.
  • Azure: The average salary for Microsoft Azure varies from around $113,582 per year.

Key features

  • AWS: Zero setting, detailed monitoring, auto-scale group.
  • Azure: Easy to start, high performance, low cost.

Long-term data protection

  • AWS: Allows long-term backup and recovery of data.
  • Azure: It does not provide long-term archiving and retrieval of data.

Security Protection

  • AWS: is provided by certain roles with authorization control capabilities.
  • Azure: Provides security by offering permissions across accounts.

Main features of Data Lake

To be classified as a data lake, a big data store must have the following three attributes:

Shared data warehouse, usually stored in a distributed file system (DFS): Hadoop data lakes store data in their natural form and capture changes in data and relative semantics throughout the data lifecycle. This approach is particularly useful for compliance checks and internal audits.

This is an improvement over the traditional enterprise data warehouse, where it is difficult to locate the real data when it is needed as the data undergoes transformation, aggregation, and modification, and the enterprise data lake tries to find the source/origin of the data.

Includes options for planning and scheduling (e.g., via scheduling tools like YARN, etc.).

Workload fulfillment is critical for Enterprise Hadoop. YARN provides resource management and a single platform to provide continuous process, security, and data management tools across the Hadoop cluster to ensure analytics workflows have the required access to data and compute power.

Contains a set of utilities and functions needed to consume, process, or work with data.

Easy and fast accessibility for users is one of the main characteristics of data lakes, so organizations store data in their natural or pure form.

Whatever the form of the data, e.g., structured, unstructured, or semi-structured, they are fed into the data lake. It enables data owners to aggregate data about customers, suppliers, and operations, removing technical or political barriers to data sharing.


  • Versatile: Powerful enough to store all types of structured/unstructured data, from CRM data to social media activity.
  • More schema flexibility: No planning or prior knowledge of data analysis is required. It keeps all data in its original form and assumes analysis will come later on request. This is very useful for OLAP. For example, the Hadoop data lake allows you to omit the schema while separating the schema from the data.
  • Real-time solution analysis: They use large amounts of consistent data and deep learning algorithms to achieve real-time solution analysis. Ability to pull values ??from unlimited data types.
  • Scalable: They are much more scalable than traditional data warehouses and less expensive.
  • Advanced Analysis / Compatibility with SQL and other languages: There are many ways to query data with a data lake. Unlike traditional data warehouses, which only support SQL for simple analysis, they offer many other options and language support for data analysis. They are also compatible with machine learning tools like Spark MLlib.
  • Data Democratization: Democratize access to data through a single, integrated data view across the enterprise while leveraging an effective data governance platform. This ensures the full availability of data.
  • Better data quality: Overall, you get better data quality with a data lake through technological advantages such as internal data storage, scalability, flexibility, schema flexibility, support for SQL and other languages, and advanced analytics.


A data lake is a storage repository capable of storing large amounts of structured, semi-structured, and unstructured data. The main purpose of building data in the lake is to provide data scientists with a view of unclean data.

The unified operations layer, processing layer, distillation layer, and HDFS are important layers of the data lake architecture. Data ingestion, data storage, data quality, data validation, data research, and data discovery are important components of data lake architecture. Data lake design should be based on what is available, not needed.

Data Lake lowers long-term operating costs and enables economical file storage. The biggest risks of data in the lake are security and access control. Sometimes can place data in unattended pools as some data may have confidentiality and regulatory requirements.