Azure Synapse Analytics vs. Hive

It's well suited for large, fastly growing, and frequently changing data warehouses (e.g., in startups). It's also suited for companies that want a single, relatively easy-to-use, centralized cloud service for all their data needs. Larger, more structured organizations could still benefit from this service by using Synapse Dedicated SQL Pools, knowing that costs will be much higher than other solutions. I think this product is not suited for smaller, simpler workloads (where an Azure SQL Database and a Data Factory could be enough) or very large scenarios, where it may be better to build custom infrastructure.

Incentivized

Hive is a powerful tool for data analysis and management that is well-suited for a wide range of scenarios. Here are some specific examples of scenarios where Hive might be particularly well-suited: Data warehousing: Hive is often used as a data warehousing platform, allowing users to store and analyze large amounts of structured and semi-structured data. It is especially good at handling data that is too large to be stored and analyzed on a single machine, and supports a wide variety of data formats. Batch processing: Hive is designed for batch processing of large datasets, making it well-suited for tasks such as data ETL (extract, transform, load), data cleansing, and data aggregation.Simple queries on large datasets: Hive is optimized for simple queries on large datasets, making it a good choice for tasks such as data exploration and summary statistics. Data transformation: Hive allows users to perform data transformations and manipulations using custom scripts written in Java, Python, or other programming languages. This can be useful for tasks such as data cleansing, data aggregation, and data transformation. On the other hand, here are some specific examples of scenarios where Hive might be less appropriate: Real-time queries: Hive is a batch-oriented system, which means that it is designed to process large amounts of data in a batch mode rather than in real-time. While it is possible to use Hive for real-time queries, it may not be the most efficient choice for this type of workload. Complex queries: Hive is optimized for simple queries on large datasets, but may struggle with more complex queries or queries that require multiple joins or subqueries.Very large datasets: While Hive is designed to scale horizontally and can handle large amounts of data, it may not scale as well as some other tools for very large datasets or complex workloads.

Incentivized

• Quick to return data. Queries in a SQL data warehouse architecture tend to return data much more quickly than a OLTP setup. Especially with columnar indexes.
• Ability to manage extremely large SQL tables. Our databases contain billions of records. This would be unwieldy without a proper SQL datawarehouse
• Backup and replication. Because we're already using SQL, moving the data to a datawarehouse makes it easier to manage as our users are already familiar with SQL.

Incentivized

• Simplicity, it offers a clean environment without risking the outcome. An example of this are the timesheets that allow a fast way to keep track of progress
• Interaction, the different options make it faster and easier to interact and collaborate in the development of a product. An example of this would be Hive Notes for meetings
• The different visualisations it offers allow to explore the best ways to affront your projects. I really like the Gantt mappings view to understand who can be contacted at each point

Incentivized

• With Azure, it's always the same issue, too many moving parts doing similar things with no specialisation. ADF, Fabric Data Factory and Synapse pipeline serve the same purpose. Same goes for Fabric Warehouse and Synapse SQL pools.
• Could do better with serverless workloads considering the competition from databricks and its own fabric warehouse
• Synapse pipelines is a replica of Azure Data Factory with no tight integration with Synapse and to a surprise, with missing features from ADF. Integration of warehouse can be improved with in environment ETl tools

Incentivized

• Organizing tasks by assignees could be better. It's a little cumbersome to check off each person you want. Can you group these?
• I don't really use any view besides task view. Is there something better I could be using?
• It would be nice if attachments showed up in a nicer format, maybe with a preview?

Incentivized

The data warehouse portion is very much like old style on-prem SQL server, so most SQL skills one has mastered carry over easily. Azure Data Factory has an easy drag and drop system which allows quick building of pipelines with minimal coding. The Spark portion is the only really complex portion, but if there's an in-house python expert, then the Spark portion is also quiet useable.

Incentivized

Its a easy tool, the best way to organize the workflow but has room for more improvements.

Microsoft does its best to support Synapse. More and more articles are being added to the documentation, providing more useful information on best utilizing its features. The examples provided work well for basic knowledge, but more complex examples should be added to further assist in discovering the vast abilities that the system has.

Incentivized

Our CSR is easily accessible and they have support built into the app itself. They also have a pretty robust support site. We also took advantage of the free trial and learned so much by putting Hive through the paces and figuring out the best way to mold it to our needs.

Incentivized

In comparing Azure Synapse to the Google BigQuery - the biggest highlight that I'd like to bring forward is Azure Synapse SQL leverages a scale-out architecture in order to distribute computational processing of data across multiple nodes whereas Google BigQuery only takes into account computation and storage.

Incentivized

Hive is a bit different than Jira and Monday, which I used mostly. Overall does a great job managing project and helps with team communication. Removes dependency of asking team members for updates by going to conference rooms. With Hive, the team updates the status, and we can easily track it.

Incentivized

Basically, the billing is predictable, and this all about it.

Incentivized

• Licensing fees is replaced with Azure subscription fee. No big saving there
• More visibility into the Azure usage and cost
• It can be used a hot storage and old data can be archived to data lake. Real time data integration is possible via external tables and Microsoft Power BI

Incentivized

• Workflow Management will help you better move your projects along which saves time and money.
• Time tracking will allow you to better manage the hours and keep your contractors accountable.
• Overall visibility of projects allow you to keep your margins down and combat "bleeding" and hidden costs or surprises.

Incentivized