Azure Data Lake is an innovative cloud platform that helps enterprises join the data-driven business environment. Azure Data Lake is a unique option to start with big data in the cloud. It offers limitless storage space for organized, semi-structured, and unstructured data. It can store any form of data, regardless of its size.
The topics covered in this blog are:
- What is a Data Lake?
- What is Microsoft Azure Data Lake?
- Azure Data Lake Analytics
- Azure Data Lake Storage (ADLS)
- Azure HDInsight
- Why Azure Data Lake?
- How Does the Azure Data Lake Work?
- Benefits of Azure Data Lake
- Use Cases of Azure Data Lake
- Azure Data Lake Pricing
What is a Data Lake?
We can describe a data lake as a repository hosting a vast volume of diverse forms of structured and unstructured data. James Dixon, CTO of Pentaho, came up with a data lake to solve the limitations of “data marts.”
The difficulty with the traditional data mart strategy is that it is only able to find answers to specified queries by just studying the subset of the characteristics. It does not enable users to get comprehensive information and insights from all accessible data.
On the other hand, adopting the data lake method together with traditional investment in data mart does not modify the status of the data and also retains the three Vs of Big Data: Variety, Volume, and Velocity. Users have access to all of the necessary tools for evaluating, querying, and processing data.
Data lakes overcome all the traditional challenges of an old-school data warehouse by decoupling data storage from query engines, offering unlimited space, unlimited file size support, read schema, move once use often, along with numerous ways to access data, which includes programming using multiple languages, REST calls, and SQL-like queries.
What is Microsoft Azure Data Lake?
Modern firms are sitting on data gold mines. The data can be structured or unstructured, and it can be in a variety of formats, including music, social media postings, texts, and more.
The Azure Data Lake is a collection of data services offered by Microsoft Azure. Data Lake services enable organizations to store, analyze, and manage a wide range of data types. The Azure Data Lake product suite gives access to many features, like Spark, U-SQL, Storm, and so on. Users may examine their own business needs and pay as they go.
Check Out: Top 30 Azure Data Factory Interview Questions.
Azure Data Lake Analytics
- Azure Data Lake Analytics is an on-demand analytics job service based on Apache Hadoop YARN that simplifies large data.
- Azure Data Lake Analytics is a compute-focused service that can simply connect to and exploit data in ADLS.
- It provides its users with real-time analytics.
- Users may grow the analytics service to meet their specific requirements.
- It’s intended to allow users to analyze data up to petabytes in size.
- For executing analytical operations, Azure Data Analytics uses U-SQL, which is a combination of C# and SQL.
- Azure Data Lake Analytics is a cost-effective analytics solution since you pay only for the processing power that you need.
Azure Data Lake Storage (ADLS)
- Azure Data Lake Store (ADLS) serves as a single repository for small and big enterprises to upload data of about an infinite size.
- Azure Data Lake Storage is a highly scalable and secure data lake that is suitable for high analytics workloads.
- Azure Lake Data Storage was formerly known as and is still sometimes referred to as, the Azure Data Lake Store.
- It supports both structured and unstructured data in its original formats.
- Azure Data Lake Storage can help optimize expenses with tiered storage and policy control.
- Users can manage and access data in Azure Data Lake Storage (ADLS) using the Hadoop Distributed File System (HDFS).
Datalake Storage Gen1 (ADLS Gen1)
- Azure Data Lake Storage Gen1 is a hyper-scale, enterprise-wide storage for big-data analytic workloads.
- It enables us to gather data of any kind, size, and ingestion speed in one single spot for operational and exploratory analytics.
- It contains all enterprise-grade characteristics such as scalability, security, management, reliability, and availability.
Key Features of ADLS Gen1
- Made for Hadoop: We can simply examine data stored in ADLS Gen1 using Hadoop analytic tools such as Hive or MapReduce.
- Unlimited storage: ADLS Gen1 provides limitless storage and can store a variety of data for analytics, ranging from kilobytes to petabytes in size.
- Highly available and Securing data: In ADLS Gen1 data are saved securely by producing multiple copies to prevent any abrupt failures.
Datalake Storage Gen2 (ADLS Gen2)
- ADLS Gen2 is a set of big data analytics capabilities. It offers various capabilities, like file system semantics, low cost, high availability, and scalability.
- It is based on Azure Blob Storage and includes all the main features of ADLS Gen1.
Key Features of ADLS Gen2
- Hadoop suitable access: ADLS Gen2 allows you to access and handle data in the same way that a Hadoop Distributed File System (HDFS) does.
- POSIX permissions: The security approach for ADLS Gen2 supports ACL and POSIX permissions together with some further granularity particular to ADLS Gen2.
- Optimized driver: The ABFS driver is created particularly for big data analytics.
Also Read: Our blog post on Microsoft Azure Certification Path 2022.
Azure HDInsight is a cluster management solution that makes it quick, fast, and cost-effective to analyze enormous volumes of data. It’s a cloud deployment of Apache Hadoop that allows customers to take advantage of optimized open-source analytic clusters for Apache Spark, HBase, Hive, Map Reduce, Kafka, Storm, and R-Server. You can support a wide variety of tasks with these frameworks, including ETL, machine learning, data warehousing, and IoT. Azure HDInsight also integrates with Azure AD for RBAC and SSO capabilities.
Why Azure Data Lake?
- Azure Data Lake contains all the features necessary to make it simple for data scientists, analysts, and developers to store data of any form, size, and speed.
- It handles all types of analytics and processing across multiple platforms and languages.
- It eliminates all of the challenges of consuming and storing all of your data while speeding up the implementation of streaming, batch, and interactive analytics.
Read More: About Azure Bastion.
How Does the Azure Data Lake Work?
Azure Data Lake is based on Azure Blob Storage, which is Microsoft’s cloud-based object storage solution. The system combines low-cost, layered storage with high-availability capabilities. It integrates with other Azure services, including Azure ADF, which is a platform for developing and operating extract, ETL, and ELT operations. The solution is based on the Apache Hadoop Yet Another Resource Negotiator (YARN) cluster management platform. It can scale dynamically among SQL servers inside the data lake, as well as servers in the SQL Data Warehouse and SQL Database.
To begin utilizing Azure Data Lake, register for a free account on the Microsoft Azure portal. You can use the portal to access all Azure services.
Benefits of Azure Data Lake
There are several benefits to using Azure Data Lake. It’s a cost-effective end-to-end big data solution that includes storage, data extraction, scalability, and other capabilities for managing all of the data your company creates. Here are the major benefits of Azure Data Lake:
- Extract data from any sources: Users have the ability to extract any form of data from structured, semi-structured, to unstructured data using Azure Data Lake with minimum effort. You can get the data from IoT devices, SQL servers, and any other sources as required.
- Pay as you go: One big benefit of Azure Data Lake is its versatility. With a pay-as-you-go strategy, you won’t have to be trapped into long-term contracts and can pay on a monthly basis. Azure Data Lake’s cost is so low that it’s even cheaper than a traditional cloud storage service. It allows businesses to upload large files at a low cost.
- Easy integration with Microsoft Big Data Platform: Another best feature is integration. Azure Data Lake will enable you to combine numerous capabilities from existing Microsoft Big Data services including Azure DataLake Analytics, Azure HDInsights, and Azure ADF.
- Security: When it comes to choosing which facility to use for a company, security is crucial. Microsoft Azure employs advanced technologies to safeguard its platform from numerous types of threats. Many major businesses trust Microsoft with their sensitive data.
- Make use of Hadoop and other amazing tools: Hadoop is an application platform that makes it easy to examine enormous amounts of unstructured data. Even non-technical people may use Azure Data Lake to employ Hadoop for the data extraction process.
Also Check: Our blog post on Azure Sentinel.
Use Cases of Azure Data Lake
- Azure manages general-purpose object storage.
- Streaming and processing of batch workloads.
- Selection of data by engineers and data analysts for particular requirements without producing copies.
Azure Data Lake Pricing
- First 100 TB = Rs. 2.58/GB
- Next 100 – 1,000 TB = Rs. 2.52/GB
- Next 1,000 – 5,000 TB = Rs. 2.45/GB
Check Out: Official Pricing Document.
Azure Data Lake is an easy-to-use solution that helps move enterprises towards a data-driven culture. There are a variety of price packages available to make its services accessible to both small and big businesses, depending on their requirements. Companies can utilize its simple, but powerful user interface to take advantage of Big Data technology and employ Azure data lake analytics features to generate unique insights and trends to achieve a competitive edge.
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