How to Succeed with Big Data and Microsoft Azure in 2021

As your business grows, it is only possible that the data keeps expanding. If your business is data-driven, you might rethink ways to store and manage your data. Cloud storage for data is essential. Big data and Microsoft Azure will help you manage data analytics. To learn more, here is a guide to maximizing Azure for your business to succeed in 2021.

Understanding Azure

Microsoft Azure offers a viable plan to manage your data. It offers the benefits of reliability, performance, scalability, and cost reduction. It is also available for any business size and model and works fine for any data analytics experience. Integration of custom modeling tools available from third-party tools such as Hadoop makes most of its services a good choice.

It is easier to outsource your data analytics by utilizing the several available data-as-a-service options within your reach. Some of the options include Cloudera, Cazina, and Qubole that you can find on the Azure marketplace.

Options

There are as many as over 50 services available for you in big data from Microsoft. However, focus on the options below and choose the right one.

Databases

Azure allows several types of databases, including self-managed table storage such as self-managed SQL servers on VMS. The managed data services include My SQL and SQL, Maria DB, and PostgreSQL.

If you are looking for something more scalable, Azure offers a fully managed data service known as Cosmos DB. It is a low latency service with global distribution and multi-master replication. In terms of API compatibility, you can use it along with Table, gremlin, Mongo DB, Ectd, Cassandra, and it supports both Apache Spark and Jupyter Notebooks.

With Azure, you can get services for data lake storage and data warehousing.

Analytics

You can get analytics from Azure through HDInsight and Azure Analysis Services.

The services bring a Bi semantic model that allows you to combine data from multiple sources into an easy-to-use model. With the services, one can easily embed interactive reports and dashboards and incorporate prebuilt database models without any need for coding or managing data yourself.

HDInsight, on the other hand, is more complex and focuses on open source analysis. It incorporates support for multiple frameworks such as Hadoop, Kafka, Spark, and Apache. With the service, you can comfortably build analytics pipelines using a range of Azure services such as Data Lake Storage and Data Factory. With HDInsight, one can use a wide range of tools, and they all support a range of common languages such as Python, Scala, R, .Net, and JavaScript.

Machine learning

Azure offers ML services such as video indexing and speech recognition, which you add to your services and applications. Azure Machine learning is a great customized ML choice. The service only allows the deployment, training, and building of models according to the spectrum of skill levels from no-code, drop interface, to code first. You also get built-in support for open-source tools. It supports tools such as Tensor Flow, ONNX, Scikit-learn, and Py Torch.

Orchestration

Azure offers two features here, the Data catalog and Data Factory.

Data catalog gives you more freedom as it is a fully managed service. It assists in data discovery and understanding of sources. The data catalog uses a crowdsourcing model of annotations and metadata to allow users to contribute.

Data storage is a server-less service for data silos. It works onsite and in the cloud. You can use it to construct ELT and ETL processes with or without scripts with over 80 connectors. When you use it alongside Azure Monitor, you can monitor pipeline performance for CI/CD.

How to succeed with Microsoft Azure

Build a common language

Big data is anything that stands to overwhelm traditional data methods. Understanding what big data is will help you build a common language.

Big data is too large, complex, and fast-moving. The data features distinct structures which need other forms of processing for business insights.

Test

It is vital to run a test program to ascertain whether the tool will work for you. You can create a small project that you can use to test the new system and get results. The small project can take a large-scale implementation.

For a test project, merge internal data, external, third-party sources. To achieve optimal results, you need to make the project as realistic as possible. The first way is to implement security and privacy policies for the project. Put in place the contingency plans and capture ROI data. Embed analytics in every process that you can.

Choose data integration and storage model

You need to choose the best data integration and storage, model. The Azure HDInsight is one of the best models you can employ. The model is Azure cloud-based fully managed services. Large amounts of data are easy to process using open source frameworks such as Hadoop, R, and Hive.

The best part about using HDInsight is that it combines data integration and storage in one solution. This tool also makes it very easy to use. The service leverages Azure’s inbuilt compliance and security controls and integrates other Microsoft tools like Eclipse, Visual Studio, and Intelli J.

Choose an Analytics program

Take your time when choosing your analytics platform. Focus on your needs and what analytic tools you can easily manage on your own. The tools depend on several factors, such as the type of data collected and the expected results. With over 50 analytic tools to pick from, you need to pick the right one.

An example is the Azure Log analytic, which allows you to collect, search and visualize machine data.

It is important to note that big data analytics requires establishments to tailor their analytics to their needs and requirements. Azure is a great medium that provides a range of solutions.

Azure offers the best solution to big data. Working in the cloud brings scalability and reduced cost that every business is looking to maximize on in every way. With Azure, you have solutions like simplified analytics that make it possible to operate big data. Whatever your configuration, you can make the most out of it and keep winning with Azure.