It's difficult to emphasize how much more data is collected today than it was a decade ago. Data is generated in a variety of places, including cell phones, automobiles, and freezers. The way data is stored has changed dramatically as well. Watch and discover how to utilize data lakes to store and analyze unstructured data from a variety of sources.
What is Azure Data Lake and how does it work?
Azure Data Lake is based on Azure Blob storage, which is Microsoft's cloud-based object storage solution. Low-cost, layered storage and high-availability/disaster recovery capabilities are among the benefits of the solution. It works with other Azure services, such as Azure Data Factory, a tool for developing and operating ETL and ELT processes.
The Apache Hadoop YARN (Yet Another Resource Negotiator) cluster management platform lies at the heart of the solution. It can scale across SQL servers in the data lake, as well as Azure SQL Database and Azure SQL Data Warehouse servers, in real-time. Large raw data storage systems for unstructured data from various sources are known as data lakes.
Create a free account on the Microsoft Azure site to get started with Azure Data Lake. You can access the gateway from there.
- Data is stored in an unprocessed, unvalidated, untransformed state.
- Massive parallel operations are supported by data lakes.
- Data is ingested from a number of different places.
- Supports a wide range of analysis tools.
A lot of people assume that Data lake is similar to Data warehouses actually it's not correct. Data lakes are not Data warehouses. Here we can see the differences between the Data Lake and Data wareHouses
| Data Lake |
Data WareHouse |
| Storage is being done for an unknown reason. |
Data is processed for querying based on a pre-defined reason for storage. |
| The data is unedited and raw. |
The data is unedited and raw. |
| This is a relatively new technology that is frequently utilized for data analytics/data scientist analysis. |
For business analysis, mature technology with a wide range of toolsets is used. |
Data lake solutions can be built with the below tools/solutions
- Azure Data Lake Storage
- Data Lake Analytics
- HDInsight
Azure Data Lake Storage
- Azure Data Lake is a cloud-based PaaS solution for huge data storage.
- Trillions of files up to a petabyte in size can be supported.
- Hundreds of gigabytes of throughput azure storage blob technology with hierarchical namespace access using the Hadoop distributed file system (HDFS).
Azure Data Lake Analytics
Azure Data Lake Analytics is a platform as a service (PaaS) that allows you to query your data lake big data.
- job-on-demand service
- resource scaling that is dynamic
- u-SQL mixes SQL and C# to provide a familiar querying syntax.
- It's also adaptable.
- Blob storage, SQL Database, and synapse analytics are also supported.
Azure HDInsight
- Azure HDInsight is a platform as a service (PaaS) for analytics.
- Hadoop's cloud platform supports spark, hive, Kafka, R, and other tools, allowing for a variety of scenarios.
- ETL (extract, transform, load) data warehousing IOT streaming machine learning/data science
Conclusion
The above article demonstrates a detailed overview of the Azure Data Lake and how does it work and when to use it if at all we are dealing with large amount of data.
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