Many companies around the glove have spent the last two decades developing relational database systems-based data warehouses and business intelligence (BI) solutions. Owing to the expense and complexity of data and databases to store unstructured data, many BI solutions have missed out on opportunities
Enters Azure Data Lake Storage that revolutionises the landscape. With a focus on high-performance big data analytics, Azure Data Lake Storage offers a repository where you can upload and store massive volumes of unstructured data. Do you want to learn more about this Microsoft Cloud Platform? Strap in as we discover everything about Azure Data Lake Storage in today’s article.
What is Azure Data Lake Storage?
A Data Lake is a collection of data stored in its natural form, commonly as blobs or discs. Azure Data Lake Storage is built into Azure for big data analytics that is comprehensive, scalable, and cost-effective.
Azure Data Lake Storage is a storage framework that combines a file system and a storage platform to help you easily find data insights. Data Lake Storage Gen2 extends the functionality of Azure Blob storage to optimize it specifically for analytics workloads. This integration enhances analytics efficiency, as well as Blob storage’s tiering and data lifecycle management capability, as well as Azure Storage’s high-availability, security, and durability.
Today, the diversity and amount of data produced and analysed is growing. Companies collect data from a variety of sources, including websites, POS systems, and, most recently, social media sites and Internet of Things (IoT) computers. Each source contributes an important piece of information that must be gathered, evaluated, and perhaps acted upon.
Key features of Data Lake Storage
Hadoop compatible access
You can manage and access data with Data Lake Storage Gen2 much as you can with a Hadoop Distributed File System (HDFS). ). The new ABFS driver (used to access data) is available within all Apache Hadoop environments. Azure HDInsight, Azure Databricks, and Azure Synapse Analytics are examples of these environments. As a result of this feature, you can store data in one place and access it through compute technologies without moving the data between environments.
A superset of POSIX permissions
The Data Lake Gen2 security model supports ACL and POSIX permissions, as well as some extra granularity specific to Data Lake Storage Gen2. Storage Explorer or frameworks like Hive and Spark can be used to configure settings.
Optimized driver
The ABFS driver was created with big data analytics in mind. The corresponding REST APIs are exposed surfaced through the endpoint dfs.core.windows.net.
Data redundancy
Data Lake Storage Gen2 uses Azure Blob replication models to provide data redundancy in a single data centre with locally redundant storage (LRS) or a secondary region using Geo-redundant storage (GRS) option. This feature guarantees that the data is both accessible and protected in the event of a disaster.
Why Use Azure Data Lake Storage?
Data Lake Storage Gen2 is optimised to deal with this variety and volume of data at exabyte scale while safely processing hundreds of gigabytes of throughput. Data Lake Storage Gen2 can be used as the foundation for both real-time and batch solutions. The below are some of the additional advantages that Data Lake Storage Gen2 provides:
Scale to match your most demanding analytics workloads
With Azure’s global infrastructure, you can meet any capacity requirements and manage data with ease. Run large-scale analytics queries at a high level of consistency. With automated geo-replication, you can scale limitlessly and have 16 9s of data durability.
Utilise flexible security mechanisms
Protect your data lake with encryption, data access and network-level control, all of which are intended to help drive insights more securely.
Access control lists (ACLs) and Portable Operating System Interface (POSIX) permissions are supported by Data Lake Storage Gen2. Permissions for data contained in the data lake may be set at the directory level or file level. This security can be configured using technologies like Hive and Spark, as well as utilities like Azure Storage Explorer. All data that is stored is encrypted at rest by using either Microsoft or customer-managed keys.
Build a scalable foundation for your analytics
If you use Data Lake Storage Gen2 or Blob storage interfaces, Azure Storage is scalable by design. It has the capacity to store and serve exabytes of data. This amount of storage is available with throughput measured in gigabits per second (Gbps) at high levels of input/output operations per second (IOPS). Processing is executed at near-constant per-request latencies that are measured at the service, account, and file levels.
Cost effectiveness
Optimise costs by scaling storage and compute separately, something you can’t do with on-premises data lakes. Use automated lifecycle management policies to optimise storage costs by tiering up or down depending on consumption
Storage capacity and transaction costs are reduced because Data Lake Storage Gen2 is constructed on top of Azure Blob storage. You don’t have to move or transform your data until you can analyse it, unlike most cloud storage systems. Furthermore, features such as the hierarchical namespace increase the overall efficiency of many analytics jobs. Because of the improved efficiency, you’ll need less computing power to handle the same volume of data, resulting in a lower total cost of ownership (TCO) for the end-to-end analytics job.
How to Create an Azure Storage Account by using the portal?
It’s easy to set up Azure Data Lake Storage Gen2. All you need is an StorageV2 (General Purpose V2) Azure Storage account with the Hierarchical namespace enabled. Let’s take a look at how to build a Data Lake Storage account in the Azure portal with the following steps:
- Firstly, Sign in to the Azure portal
- Then, select Create a resource and in the textbox that states “Search the Marketplace type Storage account, and click on Storage account.
- Next, In Storage account screen, click Create.
- Further, in the Create storage account window, in the Basics tab, under Project details section, ensure that your subscription is selected, and the appropriate resource group.
- Under the Instance section, define a storage account name. Set the Region to Central US. In the Performance radio button list, select Standard, and set the Redundancy to Locally redundant storage (LRS).
- Consequently, Select the Advanced tab. Under the section Data Lake Storage Gen2, click the checkbox next to Enable hierarchical namespace.
- Finally, Select the Review + create tab, and click Create.
Voila your new Azure Storage account is now set up to host data for an Azure Data Lake! After your account has deployed, you will find options related to Azure Data Lake in the Overview page.
Processing Big Data by using Azure Data Lake Store
Azure Storage is the foundation for building enterprise data lakes on Azure thanks to Data Lake Storage Gen2. Data Lake Storage Gen2 Designed from the start to service several petabytes of information while sustaining hundreds of gigabits of throughput. It helps you to quickly manage massive amounts of data.
Azure Data Lake Storage Gen2 plays a fundamental role in a wide range of big data architectures. These architectures can involve the creation of:
- Firstly, A modern data warehouse.
- Secondly, Advanced analytics against big data.
- Thirdly, A real-time analytical solution.
Processing big data solutions has four stages that are common to all architectures:
Stage 1 : Ingestion
The technologies and methods used to collect the source data are identified during the ingestion stage. This data will come from files, logs, and other unstructured data sources that must be stored in the Data Lake Store. Based on the frequency that the data is transferred, technology that is used will vary. Azure Data Factory, for example, could be the best technology to use for batch movement of data. Apache Kafka for HDInsight or Stream Analytics could be suitable technologies to use for real-time data ingestion.
Stage 2: Store
The store stage determines where the ingested data should be placed. We’re using Azure Data Lake Storage Gen2 in this scenario.
Stage 3: Prep and train
The prep and train stage specifies the tools that are used in data science solutions for data processing, model testing, and scoring. Azure Databricks, Azure HDInsight, and Azure Machine Learning Services are typical technologies used in this process.
Stage 4: Model and serve
Finally, the model and serve stage is concerned with the technology that will be used to present the data to the consumers. Visualization tools like Power BI, as well as other data stores like Azure Synapse Analytics, Azure Cosmos DB, Azure SQL Database, and Azure Analysis Services, are a few examples. Based on the business requirements, a combination of these technologies is sometimes used.
Use Cases
The Azure Data Lake approach is aimed at businesses who want to use big data to their benefit. It offers a data platform that allows developers, data scientists, and analysts to store data of any size and format. Also, it enables the, to perform all types of processing and analytics across multiple platforms and programming languages. It can work with your existing solutions, such as identity management and security solutions. It also integrates with cloud environments and other data warehouses. Let’s walk through all the use cases:
- Data Warehousing: Azure Data Lake Storage supports any type of data. So, it can integrate all of your enterprise data in a single data warehouse. Thus making ADLS the center of the solution for a modern data warehouse
- Internet of Things (IoT) capabilities: Azure Data Lake Storage provides tools for processing streaming data in real time from multiple types of devices.
- Support for hybrid cloud environments: With, Data Lake Storage you can use the Azure HDInsight component to extend an existing on-premises big data infrastructure to the Azure cloud.
- Speed to deployment: It’s pretty easy to get up and running quickly with the Azure Data Lake solution. All of the components are available through the portal and there are no servers to install and no infrastructure to manage.
- Advanced analytics for big data: Azure Data Lake Storage plays an important role in providing a large-scale data store. It is used in Real-time analytical solutions and advanced analytics for big data
Wrapping Up
World’s most productive Data Lake is Azure Data Lake Storage Gen2. It combines the power of a Hadoop compatible file system with integrated hierarchical namespace with massive scale and economy of Azure Blob Storage to help you transition from proof of concept to production Faster.
Azure Data Lake Storage Gen2 is a highly accessible secure, durable, scalable, and redundant cloud storage service. It’s a comprehensive data lake solution. Furthermore, creating an Azure Data Lake Storage Gen2 data store may be a useful tool in the development of a big data analytics solution. So, with Azure Certification, you can enhance your skills for Data Lake Storage. Prepare for the Microsoft DP- DP-203: Data Engineering on Microsoft Azure now!
