Azure Data Lake Analytics

To make our work more accessible and more efficient, Azure Data Lake has provided fast access to and analysis of data. This product solves our need for quick reporting on cross-platform applications and bulk data from partners. We can manage on-premises access and roles because the analytics service integrates with Azure Active Directory. There are no clusters, virtual machines, or servers to manage, maintain, or fine-tune—the utility of a highly adaptable, Azure Blob Storage-based information lake that is also secure. Azure Data Lake Analytics' simple interface makes it a reliable and easy-to-use program for beginners. SQL benefits are combined with user code flexibility through the inclusion of U-SQL. Scalable distributed runtime for U-SQL allows us to analyze data across SQL Servers in Azure (SQL database and data warehouse) in a streamlined manner.

• Easy to store for in-depth data analysis.
• User-friendly and straightforward rest API.
• SQL and C# scripting combined to make it easy to use.

• It lets us manage and scan data, making our work easy and efficient.
• It helped me manage real-time data, process it, and send it to reporting.
• Data centralization or data warehousing projects are being implemented with its help.

Databricks Lakehouse Platform (Unified Analytics Platform)

Splunk Enterprise, Microsoft Power BI, Google BigQuery, Confluent Platform

We have Azure Storage Blobs, which is traditionally one of the many ways that we would and probably more often and not the default way where we would store data. We make our data workforce by putting in Azure Storage Blobs We use the Azure SQL Database, a traditional SQL-based database. Microsoft makes that available to us in the Azure platform and we can host our data there. We also have a SQL Database, running an Azure on a Virtual Machine, if we don't want to use the Azure base SQL DB directly.

• Easy usage
• Effective data storage
• Report

• Of course price
• Documentation is not available online.
• Slow progress.

Apache Spark

a3doc cloud, Accountancy Cloud, Accio Data

My primary use case in using and investigating Azure Data Lake Analytics was in comparing how it fulfilled aggregate build in our data lake environment compared to how Databricks solved for our initial use cases. At the time, in building out a raw, refined, and curated zone before landing data in a warehouse multiple bidirectional transformation processes run between the Refined to Curated and then ultimately Warehouse layer. Key was scale, cost, and performance as compared to what can be done in processing aggregates via Databricks and opposite that ELT to a warehouse like Snowflake instead of load from lake to Microsoft Synapse.

• Uniqueness to run on a per job basis
• Security and support services (professional services) are the best in the industry.

• Has allowed us to reduce compute expenses by enabling better synchronization of workloads and user usage.
• Ease of data virtualization or rather connection of data sources from multiple locations.

Databricks Lakehouse Platform (Unified Analytics Platform) and Denodo

Databricks Lakehouse Platform (Unified Analytics Platform), Confluent Platform, Azure Bot Service (Microsoft Bot Framework), Azure Blob Storage, Pypestream, Kore.ai

We have been using Azure Data Lake Analytics as one of our data lakes, we are collecting data from many different sources, storing it on the data lake, and processing this data. As result, we have Business Intelligence tools connected to this result which we use to present some KPIs.

• Connectivity
• All tools centralized

• Since we have implemented this solution, we have been more able to follow what is going on in our process and sells.
• We are also sparing some money by comparing the costs now against the costs we had on-premise.

Alibaba Cloud Data Lake Analytics

AWS Backup, AWS CloudFormation, Amazon Kinesis, Amazon Athena, Amazon QuickSight, Amazon Elasticsearch Service, Matillion, Amazon DynamoDB, Amazon EMR (Elastic MapReduce), AWS Lambda

Used Azure Data Lake Analytics while working for a CPG major to store/process/analyze large volumes of data (daily cadence). Used Python as a programming language for processing the stored data. Also, with fluctuating data volume across weekdays/weekends, ADL analytics was helpful in processing data on demand, and scale instantly, thereby enabling us to pay for the services used/rendered.

• Ability to store data in its native format (Unstructured, semi-structured, images, online reviews)
• Scalable and flexible - according to data loads
• Cheaper storage option

• Yet to realize its full potential - Owing to skill shortage in the org
• Adoption across organization a challenge

Azure Blob Storage, Azure Data Factory and Azure Data Lake Storage

Azure Data Factory, Microsoft Power BI, Databricks Lakehouse Platform (Unified Analytics Platform), SAS Enterprise Guide