What is Data Lake | What is Azure Data Lake | What is Azure Data Lake Analytics | What is Azure Data Lake Storage | What is Azure Data Lake Storage Gen 1 | What is Azure Data Lake Storage Gen 2 | Use Cases of Azure Data lake | Advantages of Azure Data Lake | Data Lake price | How to create storage account with Azure Data Lake Storage Gen 2
The world is exploding with new data, and your company is constantly dealing with new analytics solutions such as web campaigns and device data from internet-connected products. This data is massive and comes in various formats and file types. The Microsoft Azure cloud platform has received a lot of attention recently. As a result, we must gain more insight into this cloud platform. Azure Data Lake (ADL) is a one-of-a-kind solution for getting started with big data in the cloud.
What is Data Lake?
A data lake is a consolidated storage system developed to hold, manage, and safeguard a vast volume of structured, semi-structured, and unstructured data. It has the capability to maintain data in its original format and process any type of data, regardless of its size.
- A data lake is a central storage repository that carries big data from many sources of raw data in its native form until it is needed.
- It can store structured, unstructured data, or semi-structured, which means data can be kept in a more flexible format for future use.
- A data lake is capable of storing and analyzing petabyte-size files and trillions of objects.
- It also develops massively parallel programs easily.
Check: How to connect Azure Data Lake to Azure Data Factory and Load Data
What is Azure Data Lake?
- ADL includes all the facilities required to make it easy for data scientists, developers, and analysts to store data of any shape, size, and speed.
- It does all types of analytics and processing across platforms and languages.
- It removes all the difficulties of ingesting and storing all of your data while making it faster to get up and running with streaming batch, and interactive analytics.
Read: What is Azure Event Hubs & How It Works?
What is ADL Analytics?
- ADL Analytics is an on-demand analytics job service built on Apache Hadoop YARN that simplifies big data.
- It processes big data jobs in seconds and has no infrastructure to worry about because there are no virtual machines, servers, or clusters to wait for, manage, or tune.
- It is designed to let users perform analytics on data up to petabytes in size.
- It covers U-SQL, a query language that extends the simple, familiar, declarative nature of SQL with the dramatic power of C#.
- It is a cost-effective solution for big data workloads. You pay on a per-job basis when data is processed.
Read: Azure Databricks for Beginners
What is Azure Data Lake Storage?
- ADLS provides a single repository where small or large organizations upload data of just about infinite size.
- It is designed for high-performance processing and analytics from Hadoop Distributed File System tools and applications, including support for low latency workloads.
- It allows structured and unstructured data in their native formats.
- It allows for huge throughput to boost analytic performance.
- It offers high availability, durability, and reliability.
- Azure storage services are better than Amazon S3 because it gives an integrated analytics service and places no limits on file volume.
- Types of ADLS.
- ADLS Gen1.
- ADLS Gen2.
Overview: Azure Stream Analytics
What is Azure Data Lake Storage Gen 1?
- ADLS Gen1 is an enterprise-wide hyper-scale storehouse for big-data analytic workloads.
- It permits us to capture data of any type, size, and ingestion speed in one single place for operational and exploratory analytics.
- It carries all enterprise-grade capabilities such as scalability, security, manageability, availability, and reliability.
Key Features of ADLS Gen1
Some of the key features of Data Lake Storage Gen1 include the following.
- Made for Hadoop: we can easily analyze data stored in ADLS Gen1 using Hadoop analytic frameworks such as Hive or MapReduce.
- Unlimited storage: ADLS Gen1 provides unlimited storage and can store a range of data for analytics and ranging from kilobytes to petabytes in size.
- Big data analytics: ADLS Gen1 is built for running large-scale analytic systems that require huge throughput to analyze and query large amounts of data.
- Highly available and Securing data: In ADLS Gen1 data are stored securely by making redundant copies to guard against any sudden failures.
Read: Azure SQL Database & SQL Services
What Is Azure Data Lake Storage Gen2?
- ADLS Gen2 is a collection of capabilities for big data analytics.
- It is built on Azure Blob storage and has all the key features of ADLS Gen1.
- ADLS Gen2 offers capabilities like:
- file system semantics
- file-level security
- directory
- low-cost
- scalability
- high availability/disaster recovery
Key Features of Data Lake Storage Gen2
Some of the key features of Data Lake Storage Gen2 include the following.
- Hadoop suitable access: ADLS Gen2 permits you to access and manage data just as you would with a Hadoop Distributed File System (HDFS).
- POSIX permissions: The security design for ADLS Gen2 supports ACL and POSIX permissions along with some more granularity specific to ADLS Gen2.
- Low Cost: ADLS Gen2 offers low-cost transactions and storage capacity.
- Optimized driver: The ABFS driver is developed exactly for big data analytics.
Interview Questions: Top 25 Azure Data Factory Interview Questions
Uses-Cases Of ADL
- General-purpose object storage is handled by Azure.
- Streaming and processing of batch workloads.
- Selection of data by analysts and data engineers for specific needs without making copies.
Advantage Of ADL
- Highly flexible and scalable as it is housed in the cloud.
- Allows streamlining data storage for all business needs.
- A huge amount of data can be processed simultaneously providing quick access to insights.
- Data Lake stores everything like multimedia, logs, XML, sensor data, social data, binary, chat, and people data.
- No limit on data storage and file size.
- Supports massive analytics workloads for in-depth analytics.
- It supports schema-less storage.
Data Lake Price/Month (Pay-as-you-go)
- First 100 TB: Rs. 2.58 per GB
- Next 100 TB to 1,000 TB: Rs. 2.52 per GB
- Next 1,000 TB to 5,000 TB: Rs. 2.45 per GB
Create a storage account with ADLS Gen2
1) Sign in to the Azure portal.
2) In the Azure portal, click on + Create a resource icon.
3) In the New screen, click in the Search the Marketplace text box, and type the word storage. Click on Storage account in the list that appears. Click Create
4) Fill the settings.
5) On the Advanced tab page. Click Enabled under Hierarchical namespace. Then click Review + create.
6) After the validation of the Create storage account blade, click Create.
Frequently Asked Questions (FAQs)
Q: How does Azure Data Lake Store handle data storage?
A: ADLS provides a hierarchical file system that can store data in its native format. It is designed to handle both structured and unstructured data, allowing you to store and process data of any size or type.
Q: Can I use Azure Data Lake for real-time data processing?
A: Yes, Azure Data Lake can be used for real-time data processing. You can ingest streaming data into Data Lake Store using technologies such as Azure Event Hubs or Azure Stream Analytics, and then use Data Lake Analytics to query and analyze the data in near real-time.
Q: How can I analyze data in Azure Data Lake?
A: Azure Data Lake Analytics provides a scalable and serverless analytics engine that can process and query data stored in Data Lake Store. You can use familiar languages and tools such as U-SQL, SQL, and .NET to perform advanced analytics on your data.
Q: What security features are available in Azure Data Lake?
A: Azure Data Lake offers several security features, including encryption of data at rest and in transit, integration with Azure Active Directory for authentication and authorization, fine-grained access control, and auditing capabilities to track data access and modifications.
Q: Can I integrate Azure Data Lake with other Azure services?
A: Yes, ADL integrates seamlessly with other Azure services. You can use services like Azure Databricks, Azure Machine Learning, and Power BI to perform advanced analytics, machine learning, and data visualization on the data stored in Data Lake.
Q: How can I optimize performance in Azure Data Lake?
A: To optimize performance in ADL, you can leverage techniques such as partitioning and indexing to improve query performance, use compression to reduce storage costs, and take advantage of parallel processing capabilities offered by Data Lake Analytics.
Related/References
- Microsoft Azure Data Engineer Associate [DP-203]: Everything You Need To Know
- Microsoft Certified Azure Data Engineer Associate | DP 203 | Step By Step Activity Guides (Hands-On Labs)
- Batch Processing Vs Stream Processing: All You Need To Know
- Introduction to Big Data and Big Data Architectures
- Designing And Automate An Enterprise BI solution In Azure
Next Task For You
In our Azure Data Engineer training program, we will cover 28 Hands-On Labs. If you want to begin your journey towards becoming a Microsoft Certified: Azure Data Engineer Associate by checking out our FREE CLASS.
Leave a Reply