Azure for big data analytics
In an increasingly data-driven business climate, companies are focusing more on generating valuable insights from their huge volumes of data. As of 2017, 75% of firms had already invested in data analytics solutions, according to Gartner. Amongst the many cloud vendors available, the majority of enterprises are using the Microsoft Azure cloud platform to build their sophisticated big data & analytics solutions because it offers the most comprehensive offering needed to build a robust big data & analytics solution.
Are you also considering Azure cloud services for your big data analytics? This article will touch on eight popular big data analytics solutions available on Microsoft Azure, as well as their differences and the typical use cases for each option. Keep reading to learn more!
1. Azure Synapse Analytics
Azure Synapse Analytics is a large-scale data analysis service that combines business data storage with macro or big data analysis, and was presented as an upgrade of Azure SQL Data Warehouse (SQL DW). When it comes to processing, maintaining, and serving data for urgent business intelligence and data prediction needs, Synapse offers a single service for all workloads. Synapse’s ability to integrate mathematical machine learning models using the ONNX format allows it to integrate with Power BI and Azure Machine Learning, which is made feasible by its integration with Power BI and Azure Machine Learning.
2. Azure Databricks
Databricks is an Apache Spark-based analytics service. Apache Spark is a well-known technology for quickly processing large volumes of unstructured data. Databricks supports a variety of languages and frameworks, including Python, Scala, Java, SQL, and R, as well as AI/ML libraries such as TensorFlow and PyTorch, allowing you to work with Spark data in any of them.
Databricks also interfaces with Azure Machine Learning (see below), allowing you to access a wide number of pre-trained machine learning algorithms.
Databricks makes it easy to set up managed Apache Spark clusters with auto-scaling and auto-termination, removing the hassle of installing Spark in your own data center.
3. Data Lake Analytics
Azure Data Lake Analytics allows you to create data transformation algorithms in a range of languages, including U-SQL (a Microsoft-developed language that combines the advantages of SQL and C#), Python,.NET, and R. It has the ability to process petabytes of data.
Data Lake Analytics differs from Azure Synapse Analytics in that it does not collect and process all of your data in a data lake. Instead, it links to Azure-based data sources, such as Azure Data Lake Storage, and uses code you give to execute on-the-fly analytics.
4. Azure Stream Analytics
With Azure Stream Analytics, you can create an end-to-end streaming event pipeline. It is built on a serverless platform. Stream Analytics allows you to create an analytics pipeline for streaming data that uses SQL syntax for data processing and can be deployed in minutes. It flexibly scales up in response to the volume and throughput of your streaming data.
Because streaming data typically needs high-performance processing and real-time responses, Azure Stream Analytics offers sub-second latency and event processing that is guaranteed “once-and-for-all.” It also comes with a 99.9% uptime guarantee.
5. Azure HDInsight
In the preceding decade, Apache Hadoop was a massive thing for big data, and while its popularity has waned, the Hadoop ecosystem remains tremendously powerful. It enables you to carry out complicated, distributed analysis jobs on nearly any amount of data. HDInsight allows you to quickly establish Hadoop large data clusters and scale them up or down based on your requirements. It connects with other Azure services like Data Factory and Data Lake Storage, allowing you to apply Hadoop analytics to existing data.
Apache Spark, Apache Kafka, HBase, Hive, and Storm, as well as other prominent Hadoop technologies, are all included in HDInsight. Azure redundancy solutions enable monitoring, security, compliance, and high availability as part of the enterprise-scale infrastructure.
6. Azure Data Factory
Azure Data Factory offers the Extract Transform Load (ETL) service. ETL is a term that dates back to the days when large-scale structured data processing was commonplace. A data cleansing and conversion technique (ETL) cleans and transforms data from a structured database into a format that can be analyzed. Data Factory’s visual editor allows you to design ETL and Extract Load Transform (ELT) strategies without writing code or configuring anything.
Over 90 data sources, including Amazon S3, Google BigQuery, and numerous on-premise data sources, have built-in connectors in Data Factory. Data from Data Factory can also be copied to Azure File Storage.
7. Azure Machine Learning(Azure ML)
This is a vast collection of pre-packaged and pre-trained machine learning algorithms. It also gives users a way to consume and apply these algorithms to real-world situations. The user-friendly machine learning UI in Azure ML speeds up model development by allowing you to establish machine learning pipelines that incorporate different algorithms and processes like model training, testing and evaluation.
Azure ML also provides interpretable AI solutions. It contains visualizations and other data that may be used to better understand model behavior, apply fairness metrics, and compare algorithms to see which one is the best.
8. Azure Analysis Services
The Azure Resource Manager, which aggregates data from numerous sources and provides a single trusted semantic model, can be used to build up Azure Analysis Services. It enables you to create high-performance business intelligence products with secure access and a short time to market. It scales up and down in response to analytical workload, and you only pay for what you use. You can also use Analysis Services to import existing models or SQL Server 2016 tabular models.
Getting started is easy.
Processing big data in real-time is now an operational necessity for many businesses and the earlier you incorporate an advanced analytical solutions into your growth strategy, the sooner you will be able to analyze marketing performance and user behavior, to gain a competitive advantage.
However, before experimenting with any of the above analytics services and solutions, make sure to thoroughly assess your demands and requirements. Keep in mind that big data’s architecture is already complex, so adding more tools should be done with caution.
On that note, leave us a message and our data experts will assist you to select an optimal Azure analytics solution to help scale your business.