Azure Synapse Analytics

Awesome implementation of Data Warehouse and Apache Spark Capabilities!

Kommentare: We are using Azure synapse to build out data pipelines. There are a lot of activities and templates available for no-code data movement setup. You can migrate from Databricks if you want.

Vorteile:

Azure Synapse Analytics, according to me is very easy and convenient implementation of spark, SQL and data warehousing technologies.- You get notebooks to write ETL code and run it on distributed spark clusters.- Ingesting data to SQL pool is very easy.- Awesome and dedicated support via technical staff.- East US region now has Hardware Accelerated Spark Pools available for Deep Learning Capabilities. - All artifacts can easily be created via ARM templates.

Nachteile:

There are no cons, but there are few difficulties that I have faced:1) In Pipelines, when notebook activities are being triggered it should have a flag that makes2) When a notebook job is submitted to spark pool, session takes too long time to be spin up. Can't they move it to Kubernetes instead of traditional VMs?

Low Costs Data Mining

Vorteile:

To host the resources that create high costs when hosted within the company on the cloud, to process them and to reach the desired reports.

Nachteile:

There is no negative side except that many data are outside the company due to its nature.

Azure Nightmare

Kommentare: It wasn't the best I have dealt with better softwares in the past honestly

Vorteile:

The match and merge was amazing and the data integration was really good as well

Nachteile:

It was way too expensive for something that still had quite a few bugs in its software that needed worked out

My take on Azure Synapse

Kommentare: A nice analytics platform which is a tool or a service that does multiple work so we need not download external softwares or use other services for each of the task.

Vorteile:

Nice for data analysis even when it is at a large scale. Synapse has good user and data management capabilities with indexes that can be defined to the data in the platform. Also it integrates seamlessly with GIT so we can access anything from it with the help of few commands. Data management at all levels are accessible be it the table level or schema level, can import large datasets from various sources like Data Factory, Power BI and various other sources.

Nachteile:

Finding out and debugging errors can sometimes be clueless and takes a lot of time for the queries to run and execute at times.

Stable, cloud-based data warehouse service that continuously evolves

Vorteile:

Azure simplifies the creation, testing and management of cloud assets for us. It is relatively easy to use and offers full features for hosting web applications, websites, APIs, and a ton of others. It has good compatibility Windows and has been a fairly stable space in all the time we have worked with it. The ability to scale the services according to our needs is great especially for SME who may not have a physical office because we can eliminate the need to set-up physical servers. We can work from anywhere and access our files in the cloud which is very convenient.

Nachteile:

For people who aren’t very big fans of Microsoft, working with this program might not be their cup of tea either. But for those who live and breathe Microsoft, this is sheer heaven. The sheer number of functions, services, apps make it hard to identify what the most effective solution is, but they try to compensate for this by coming up with pre-packaged solutions.