Creating a Stored Procedure in Azure Data LakeStored Procedures provide a way to run tasks, such as extracting data from files and inserting it into tables.
Data Lakes allow you to store relational data like operational databases and data from line of business applications, and non-relational data like mobile apps, IoT devices, and social media. They also give you the ability to understand what data is in the lake through crawling, cataloging, and indexing of data.
You can use the MongoDB Query Language (MQL) on Atlas Data Lake to query and analyze data on your data store. Atlas Data Lake supports most, but not all the standard server commands. You can run up to 30 simultaneous queries on your Data Lake against: Data in your S3 bucket.
Data lakes are next-generation data management solutions that can help your business users and data scientists meet big data challenges and drive new levels of real-time analytics. They provide the framework for machine learning and real-time advanced analytics in a collaborative environment.
The Amazon Simple Storage Service (S3) is an object storage service ideal for building a data lake. The centralized data architecture of an S3 data lake makes it simple to build a multi-tenant environment where multiple users can bring their own Big Data analytics tool to a common set of data.
Azure Synapse uses Azure Data Lake Storage Gen2 as a data warehouse and a consistent data model that incorporates administration, monitoring and metadata management sections.
Azure Data Lake is a cloud platform designed to support big data analytics. It provides unlimited storage for structured, semi-structured or unstructured data. It can be used to store any type of data of any size.
HDInsight provides a greater range of analytics engines including HBase, Spark, Hive, and Kafka. However, HDInsight is provided as a PaaS offering and therefore requires more management and setup.
Microsoft Azure Data Lake Storage (ADLS) is a fully managed, elastic, scalable and secure file system that supports HDFS semantics and works with the Apache Hadoop ecosystem. It provides industry-standard reliability, enterprise-grade security and unlimited storage that is suitable for storing a large variety of data.
From our simple example, we identified that Data Lake Analytics is more efficient when performing transformations and load operations by using runtime processing and distributed operations. On the other hand, Databricks has rich visibility using a step by step process that leads to more accurate transformations.
Azure Data Lake is built to be part of the Hadoop ecosystem, using HDFS and YARN as key touch points. Azure Data Lake uses Apache YARN for resource management, enabling YARN-based analytic engines to run side-by-side.
A data lake stores large volumes of structured, semi-structured, and unstructured data in its native format. Data lake architecture has evolved in recent years to better meet the demands of increasingly data-driven enterprises as data volumes continue to rise.
The ability to store files of arbitrary sizes and formats makes it possible for Data Lake Storage Gen1 to handle structured, semi-structured, and unstructured data. Data Lake Storage Gen1 containers for data are essentially folders and files.
Azure Data Lake Store Gen2 is a superset of Azure Blob storage capabilities.
Azure Blob Storage is a general purpose, scalable object store that is designed for a wide variety of storage scenarios. Azure Data Lake Storage Gen1 is a hyper-scale repository that is optimized for big data analytics workloads. ACLs based on Azure Active Directory Identities can be set at the file and folder level.
You can upload your data to a Data Lake Storage Gen1 account directly at the root level or to a folder that you created within the account.
- From the Data Explorer blade, click Upload.
- In the Upload files blade, navigate to the files you want to upload, and then click Add selected files.
Steps for Data Lake creation
- Create the right business justification and treat it as a business project – not a technology project.
- Build an architecture that will support your data.
- Pick a data governance tool.
- Create your Data Lake in stages:
Connect to Your Data Lake Using the MongoDB Shell
- Open the Connect dialog.
- Click Connect with the MongoDB Shell.
- Click I have the MongoDB Shell Installed.
- Select mongosh from the dropdown.
- Copy the provided connection string to your clipboard.
- Paste and run your connection string in your terminal.
In the Get Data dialog box, click Azure, click Azure Data Lake Store, and then click Connect. If you see a dialog box about the connector being in a development phase, opt to continue. In the Azure Data Lake Store dialog box, provide the URL to your Data Lake Storage Gen1 account, and then click OK.
‎Azure Data Lake Storage Gen2 is a set of capabilities dedicated to big data analytics, built on Azure Blob Storage. Data Lake Storage Gen2 converges the capabilities of Azure Data Lake Storage Gen1 with Azure Blob Storage.
