Many businesses around the world have spent the past two decades developing relational database systems-based business intelligence (BI), and data warehouses. Many BI solutions have missed opportunities due to the complexity and cost of data storage and databases to store unstructured information.
Azure Data lake Storage is changing the landscape. Azure Data Lake Storage is a repository that allows you to upload and store large volumes of unstructured data. It focuses on high-performance big-data analytics. Want to learn more about the Microsoft Cloud Platform? Let’s get started as we learn everything about Azure Data Lake Storage.
What is Azure Data lake Storage?
A Data Lake is a collection data stored in its original form, often as blobs and discs. Azure Data Lake Storage is built into Azure to provide big data analytics that is comprehensive and scalable. It is also cost-effective.
Azure Data Lake Storage is a storage system that combines a filesystem and a storage platform to make it easy to find data insights. Data Lake Storage Gen2 extends Azure Blob storage’s functionality to make it more efficient for analytics workloads. This integration improves analytics efficiency as well as Blob storage‚Äôs tiering, data lifecycle management capability, and Azure Storage’s high availability, security, durability, and durability.
The number and variety of data being produced and analysed is increasing. Companies can collect data from a variety of sources including websites, POS systems and, most recently social media sites and Internet of Things computers. Each source provides an important piece that must be gathered and evaluated before being acted upon.
Data Lake Storage: Key features
Access compatible with Hadoop
Data Lake Storage Gen2 is as easy to use and manage data as a Hadoop Distributed File System. ). All Apache Hadoop environments now have the new ABFS driver, which allows data to be accessed. These environments include Azure HDInsight and Azure Databricks. This feature allows you to store data in one location and access it through compute technology without having to move the data between environments.
A superset POSIX permissions
Data Lake Gen2 security models support ACL and POSIX permissions as well as extra granularity for Data Lake Storage Gen2. Configuration settings can be made using Storage Explorer or frameworks such as Spark and Hive.
Optimized driver
The ABFS driver was designed with big data analytics and in mind. The corresponding REST APIs are exposed surfaced through the endpointdfs.core.windows.net.
Data redundancy
Data Lake Storage Gen2 uses Azure Blob replica models to provide data redundancy within a single data center with locally redundant storage (LRS), or in a secondary region using Georedundant storage option (GRS). This feature ensures that data is both accessible as well as protected in the event a disaster strikes.
Why use Azure Data Lake Storage?
Data Lake Storage Gen2 is optimized to handle this volume and variety of data at exabyte-scale while safely processing hundreds upon hundreds of gigabytes. Data Lake Storage Gen2 is a foundation for batch and real-time solutions. Below are some additional benefits that Data Lake Storage Generation2 offers:
Scale to meet the most challenging analytics workloads
Azure’s global infrastructure allows you to meet any capacity requirement and manage data with ease. Large-scale analytics queries can be run with high consistency. Automated geo-replication allows you to scale infinitely and has 16 9s data durability.
Flexible security mechanisms are a must
Protect your data lake by using encryption, data access, and network-level control. These are all intended to help you drive insights more securely.
Data Lake Storage Gen2 supports access control lists (ACLs), and portable operating system interface (POSIX), permissions. Data Lake Storage Gen2 supports permissions for data stored in the data lake at either the file or directory level. This security can be achieved using technologies such as Spark and Hive, as well as utilities such as Azure Storage Explorer. All data is encrypted at rest using either Microsoft keys or customer-managed keys.
Your analytics should be scalable
Azure Storage can be scaled by design if you use Blob storage interfaces or Data Lake Storage Gen2. It can store and serve large amounts of data. This storage capacity is available at gigabits per seconds (Gbps) and high levels of input/output operation per second (IOPS). Processing takes place at near constant per-request times that are measured at the service level, account level, and file level.
Cost effectiveness
Scale storage and compute separately to optimize costs, which is not possible with on-premises data lakes. To optimize storage costs, use automated lifecycle management policies. Tier up or down according to consumption.
Data Lake Storage Gen2 uses Azure Blob storage to reduce transaction costs and storage capacity. Unlike most cloud storage systems, you don’t need to move or transform data before you can analyze it. Features such as the hierarchical namedspace improve the efficiency of many analytics jobs. The improved efficiency means that you will require less computing power to handle the same amount of data. This results in a lower total cost-of-ownership (TCO) for the entire end-to-end analysis job.
How to create an Azure Storage Account using the portal
It is easy to set Azure Data Lake Storage Gen2 up. All you need to set up Azure Data Lake Storage Gen2 is a StorageV2 (General Purpose Version V2)Azure Storage account that has the Hierarchical namespace enabled. Let’s take a look how to create a Data Lake Storage account using the Azure portal.
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