The broad Azure stack gives many solutions to different business problems through its services offerings. One of its data analytics services, Azure Data Explorer, focuses on the exploration of big data in its raw state or as it is being streamed in large volumes, in real-time, from various sources.
Azure Data Explorer also offers the ability to ingest and store data within its own database and allows analysis through ad-hoc querying and even in-built dashboards, charts, pivots and more. The in-built operators allow the creation of time series and time-related analysis over big data, which help in detecting anomalies and forecasting. Additionally, it integrates well with other external dashboard tools including Power BI.
Apart from the different modes of ingestion that it offers within itself, one could also orchestrate the ingestion via Azure Data Factory (ADF), using the Azure Data Explorer activity that can be found in ADF pipelines. Likewise, ingestion could be done via the utilisation of other Azure services like Event Hub or directly from an Azure Blob Storage.
All this shows how easy it is to integrate Azure Data Explorer even within an existing and already functioning data architecture.
Azure Data Explorer is built on top of the Kusto engine providing a robust computation and processing power to get analytical results on big data in short time. Kusto has its own querying language, called Kusto Query Language (KQL) which is simpler to learn and use than other querying languages like SQL.
An overview of this service has been provided by one of my colleagues Alex Lai in a blog post, a few months after it was announced by Microsoft.
With the service evolving further since, and with a potential of it evolving even more, Adatis have covered the functionality, features and use cases of Azure Data Explorer in more depth in a whitepaper.
The whitepaper highlights the key benefits that distinguish Azure Data Explorer as an exploration service and covers other related services that are built on the same Kusto engine that also allow analysis via KQL, particularly Azure Sentinel.
Have a read and see how Azure Data Explorer can help you explore your data better and quicker!
Meet the Team – Matt How, Principal Consultant
Next up in our series of meet the team blogs is Matt How. Matt has
Apr
MLFlow: Introduction to MLFlow Tracking
MLFlow is an open-source MLOps platform designed by Databricks to enable organisations to easily manage
Apr
Adatis are pleased to announce expansion plans into India
Adatis has offices in London, Surrey and Bulgaria – and has some big expansion plans
Mar
Querying and Importing Data from Excel to SSMS
Introduction There are couple of ways to import data from Excel to SSMS – via
Mar
Data Engineering for Graduates and New Starters
This blog aims to give potential graduates and other new starters in the industry some
Mar
Passing DP-900 Azure Data Fundamentals
Back in December, I took the DP-900 Azure Data Fundamentals exam which is one of
Feb
Real-time Dashboards Provide Transparency to Everyone
Real-time dashboards enable data analytics firm Adatis to be agile and transparent with its team
Feb
Data is creating a new age of business agility
Developing a data strategy is central to ensuring your business can respond to disruptions and
Feb