Azure Data Lake Analytics vs. DeepIQ

Azure Data Lake Analytics services are beneficial when working with a lot of data. It can process enormous amounts of data extremely quickly. Service is secure and easy to set up, build, scale, and run on Azure. Regarding big data analytics and reporting, parallel processing has a significant impact. It consolidated our analytics from multiple systems and increased our analysis productivity. This tool has excellent support for reporting tools like Power BI and is very quick when performing analytics.

Incentivized

• Process large data transformation jobs using pretty much any language needed.
• Native integration with Azure storage.
• Top notch security that fulfills all audit needs.
• Easy to consolidate enterprise data under one location - Single source of truth.

Incentivized

• There's a bit of bias towards cloud with ADL Analytics. Depending upon a company's infra strategy and investment plans, there are some challenges with migration and integeration.
• Not worth the time/effort/money if the organization doesn't have "Volume" of data. Cost effective only when daily loads exceed around 1million.
• While training materials are available online, Adoption rate - Yet to pick up.

Incentivized

We did some research about Alibaba Cloud Data Lake Analytics and even being cheaper than Azure Data Lake Analytics, we decided to go for the second one once we noticed they have more features and better documentation. Another thing we considered during this process was the fact that we have more people that already have Azure Cloud knowledge.

Incentivized

• 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.

Incentivized