Hybrid Transactional and Analytical Processing, or HTAP, is an advanced database capability that allows for both types of workloads to be performed without one impacting the performance of the other.
In this Video Blog, I cover some of the history of HTAP, some of the challenges and benefits of these systems, and where you can find them in Azure.
Azure Synapse Link allows you to connect to your transactional system directly to run analytical and machine learning workloads while eliminating the need for ETL/ELT, batch processing and reload wait times.
In this vLog, I explain how to turn the capability to use Link on in CosmosDB, and what’s happening under the covers to give access to that analytical workload without impacting the performance of your transactional processing system.
Check it out here and let me know what you think!
In my latest video blog I discuss getting started on the newly Generally Available Spark Pools as a part of Azure Synapse, another great option for Data Engineering/Preparation, Data Exploration, and Machine learning workloads
Without going too deep into the history of Apache Spark, I’ll start with the basics. Essentially, in the early days of Big Data workloads, a basis for machine learning and deep learning for advanced analytics and AI, we would use a Hadoop cluster and move all these datasets across disks, but the disks were always the bottleneck in the process. So, the creators of Spark said hey, why don’t we do this in memory and remove that bottleneck. So they developed Apache Spark as an in memory data processing engine as a faster way to process these massive datasets.
When the Azure Synapse team wanted to make sure that they were offering the best possible data solution for all different kinds of workloads, Spark gave the ability to have an option for their customers that were already familiar with the Spark environment, and included this feature as part of the complete Azure Synapse Analytics offering.
Behind the scenes, the Synapse team is managing many of the components you’d find in Open-Sourced Spark such as:
I hope you enjoy the post. Let me know your thoughts or questions!
In my latest video blog I discuss and demonstrate some of the ways to connect to external data in Azure Synapse if there isn’t a need to import the data to the database or you want to do some ad-hoc analysis. I also talk about using COPY and CTAS statements if the requirement is to import the data after all. Check it out here
In this vLog post I discuss how Azure Synapse, Databricks and Snowflake compare when it comes to common data workloads:
Data Science
Business Intelligence
Ad-Hoc data analysis
Data Warehousing
and more!
In this vLog I give an overview of Azure Data Explorer and the Kusto Query Language (KQL). Born from analyzing logs behind Power BI, ADX is a great way to take large sets of data and quickly analyze those datasets and get actionable insights on that data.
Find more details about Azure Data Explorer here: https://azure.microsoft.com/en-us/services/data-explorer/
And get started with these great tutorials: https://docs.microsoft.com/en-us/azure/data-explorer/create-cluster-database-portal
In this vLog, I cover the reasons why you might consider using Azure Data Factory, a mature cloud service for orchestration and processing of data over the newly GA Azure Synapse Studio.
Synapse has all of the same features as Azure Data Factory, but if you have a large development team working on ELT operations, or a simple data processing activity, it could make sense for the less-cluttered Azure Data Factory.
Take a look at the vLog here and let me know your thoughts on other scenarios for you!
In this vLog I describe some of the leading Master Data Management (MDM) tools in Azure, a necessary part of any Data Warehouse deployment, but often overlooked.
To be a solution architect, you’ve got to have great written and oral skills to be able to leverage your technical experience in helping to develop a solution with a team. In this video I cover some items you will want to focus on if you’re looking for a career change where you can leverage your technical acumen:
In this video blog post I covered the serving layer step of building your Modern Data Warehouse in Azure. There are certainly some decisions to be made around how you want to structure your schema as you get it ready for presentation with whatever your business intelligence tool of choice, for this example I used Power BI, so I discuss some of the areas you should focus on:
