Efficient ways to implement big data analytics in Microsoft Azure

Introduction

Businesses run on big data today; the insights derived from processing raw data help organizations deliver better solutions and grow exponentially.

Efficient big data solutions are necessary to realize the latent benefits. Microsoft Azure provides a powerful, accelerated analytics toolkit that delivers enriched insights from raw data.

Let’s understand how to best implement Microsoft Azure for big data analytics.

Azure Big data analytics best practices

Azure big data analytics offers countless benefits to several industries, including retail, healthcare, manufacturing, finance, and more, enabling them to find new opportunities and identify areas of improvement. As the field of data analytics is rapidly evolving, the concept of Azure big data best practices is also changing. However, we have distilled the most important best practices or fundamental principles that’ll help strategize Azure big data analytics for your business just right. 

Microsoft suggests a four-step process to build a new big data solution on the Azure cloud:

1. Evaluation

The first step is to analyze your business goals and define big data strategy based on the former. Next, gather, analyze, and understand your business requirements clearly, which will help you understand the type of data you want and how to format them. Finally, the type and amount of data will help you streamline the data ingestion process and the type of storage required. 

2. Architecture

Come up with an initial architecture based on the results of your evaluation. The architecture should encompass both your business and big data goals. Around your local data center, define the big data infrastructure, skills, and expertise expected of your development and operations teams. 

3. Production

The next step is to configure and prepare your production environment, which relies on the Azure service, data source combination, and whether you opt for a pure cloud or hybrid model. Azure Monitor and Log Analytics can help you monitor processes to get the best performance and return on investment. Enforce privacy and security policies, and consider disaster recovery, backup, and restoration for your big data system.

Additionally, consider the cost factor to ensure if it aligns with your budget.

4. Cloud services or infrastructure cost

Finally, building your business on a cloud foundation helps you overcome digital infrastructure boundaries. However, you need to plan, analyze, and reduce spending to maximize cloud investment and ensure your organization is prepared for success. Implement cost management governance best practices, cost controls, and guardrails for your environment to mitigate cloud spending risks. You need to set budgets and allocate spending to different teams and projects. Microsoft has privateed a lot of resources that will help optimize your Azure costs.

Big Data analytics: The Azure process

Microsoft Azure tools provide analytics capabilities at every step of the data journey throughout an organization:

1. Ingest	2. Store	3. Prep and Train	4. Serve
Azure Data Factory is used for ingesting data streams.	Azure Data Lake stores data from disparate sources.	Azure Machine Learning and Azure Databricks transform and enrich the data stored in Azure Data Lakes. Azure Databricks is additionally involved in ad-hoc analysis through Power BI and Azure Synapse Analysis tools.	Azure Kubernetes and operational databases (Cosmos DB, SQL DB) serve the data to enterprise apps.

Microsoft Azure tools to aid Big Data analytics

Microsoft Azure is a powerhouse suite that enables businesses to leverage their analytics capabilities through eight different tools that transform the business into an analytics-driven organization.

1. Azure Synapse Analytics

Azure Synapse Analytics is a boundless analytics service that combines big data analytics, data integration, and enterprise data warehousing with a unified experience to ingest, prepare, transform, manage, and serve data for immediate machine learning and BI needs. In addition, synapse allows organizations to choose between serverless or dedicated data querying, which is scalable according to the company’s requirements.

Azure Synapse Studio is the heart of Azure Synapse Analytics, an accessible collaboration workspace for implementing and managing Azure cloud-based analytics. Various analytics runtimes of Azure Synapse Analytics, such as Apache Spark and SQL, connect through a single platform to enhance collaboration among data professionals working on advanced analytics solutions.

Companies get access to the following powerful analytics features of Synapse:

• Streaming analytics
• Integration with Azure Machine Learning
• Codeless AI
• Integration with Microsoft Power BI
• Industry-specific database templates
• Code-free ETL/ELT data integration
• Unified management
• Intelligently-managed data warehouse workloads
• Serverless exploration of data lakes through T-SQL 
• Performance accelerator for Power BI

2. Azure Databricks

Databricks is a crucial analytics tool in Azure. It is powered by Apache Spark, a polyglot engine that supports the execution of data engineering, data science, and machine learning on a single node or a cluster. Apache Spark equips Azure Databricks with large-scale SQL capabilities and batch and stream processing.

The tool supports Java, SQL, Scala, Python, TensorFlow, PyTorch, R, and Scikit Learn. With Azure Databricks, companies can prepare their big data, transform it into insights, and enrich it with AI solutions for better usability.

3. Azure HDInsight

Azure HDInsight is a full-scale, cloud-based, managed service for analytics that businesses can use through open-source frameworks (like Apache, Hadoop, Hive, or Spark). Enterprises can easily manage big data by creating clusters on one of these frameworks and scaling them as needed.

• Hadoop adopts YARN and HDFS management for batch data process and analysis (in parallel).
• Apache Spark allows businesses to conduct parallel processing with their data to boost big data analytics performance.

4. Azure Data Factory

Big Data is unusable in its nascent form—unorganized, unstructured, and completely random. To derive actionable business insights from it, organizations need tools like Azure Data Factory.

• Data Factory connects with disparate data sources and processors of a company. It can ingest copious amounts of data from multiple sources in a cloud, hybrid, or on-premise format.
• It can transform unstructured data into digestible formats and enrich it. The ADF mapping data flow process transforms raw data into usable information.
• Data Factory supports continuous integration and continuous delivery (CI/CD) for data using Azure DevOps and GitHub for data integration pipelines.
• It monitors scheduled activities on the data pipeline.

5. Azure Machine Learning

Big Data analytics requires intelligent, objective-focused data processing engines. Azure Machine Learning allows a business to manage its ML projects by training and deploying models in data science.

Azure provides Machine Learning Studio with “notebooks” that allow businesses to write their ML code in managed servers. This tool can help visualize and run metrics for analysis and experimentation in data visualization.

6. Azure Stream Analytics

Stream processing takes multiple parallel actions on a data stream as soon as it is created. With Azure Stream Analytics, businesses can analyze data streams with latencies below the millisecond mark. In addition, companies can accomplish the following:

• Identify relationships and patterns in large volumes of data produced simultaneously across sources.
• Use data to create action triggers or workflow initiations (such as alerts, notifications, or reporting)
• Store the streamed data for future use with intelligent analytics engines.

7. Data Lake Analytics

Azure Data Lake Analytics is a sought-after analytics service that lets you develop data transformation and processing programs in U-SQL, Python, R, and .Net over petabytes of data. Since U-SQL is a simple yet extensible language, it allows you to write code once and parallelize automatically based on your scaling requirements. In addition, you can process data for various workloads such as ETL, querying, machine learning, machine translation, analytics, image processing, and sentiment analysis leveraging existing libraries written in Python, R, or .Net languages.

Data Lake Analytics and Azure Synapse Analytics are different. The former does not drag your data into the data lake to process it. Instead, it connects to the Azure data sources, such as Azure Data Lake Storage, and then performs analytics on the run using the code you provide.

8. Azure Analysis Services

Businesses requiring a fully managed PaaS (Platform-as-a-Service) tool for analytics can leverage Azure Analysis Services. It uses advanced modeling and mashup for data unification, metrics, and security by creating singular, semantic, tabular data models. In addition, businesses have the flexibility to scale up or down according to requirements (since Azure Analysis Services is a PaaS).

Azure, Big Data, and LevelShift

Microsoft Azure is a full-scale cloud computing solution providing businesses with extended functionality, essential tools, and improved computing. With LevelShift, organizations can seamlessly implement big data analytics through Microsoft Azure at an enterprise scale.

We help businesses customize solutions to be the perfect fit. To learn how LevelShift can support enterprises with Microsoft Azure implementation for big data analytics, visit this page or connect with our experts.