Everyone knows what Microsoft Azure is: a cloud computing service by Microsoft providing Software as a service (SaaS), Platform as a service (PaaS), and Infrastructure as a service (IaaS) models for deployment and hosting applications. Microsoft Azure is one of the fastest-growing cloud services providers in the world and there are a lot of questions surrounding Microsoft Azure and its services. So, start here if you want to understand Microsoft Azure better.
What is Cloud Computing?
Cloud computing involves users renting services like the type of servers, storage, databases, networking, software, analytics, and intelligence. It is to ensure that the storage, distribution, and communication throughout the entire organization happens through the cloud thus reducing the requirement of having a physical server.
The advantages of using azure cloud computing are manifold, starting with the pay-per-use options which considerably reduce the infrastructure costs, and also provide the scope of increased scalability over time. It also reduces organizations' deployment time as they wouldn’t need much maintenance. Overall, it allows users to pay as per their needs and requirements.
How is Azure Synapse Analytics different from Azure Databricks?
Azure Synapse as an end-to-end analytics solution has the ability to query relational and non-relational data at a petabyte scale. Synapse helps perform SQL queries. Meanwhile, Azure Databricks which is based on open-source Apache Spark is used for batch processing or stream processing on big data.
Azure Synapse architecture comprises the Storage (with Azure Data Lake Storage), Processing, and Visualization layers (with Power BI). Azure Databricks though it doesn’t contain a Data warehouse accompanies Lake House architecture. Another difference lies with the Git collaboration between the two. Azure Synapse has built-in support for Azure ML but not Git environment, whereas Databricks incorporates optimized ML workflows and facilitates tight version control using Git.
Differences between Azure Synapse Analytics vs Azure Data Factory
Azure Data Factory, majorly used for data transformation and data integration is a platform like SSIS that allows developers to integrate multiple data sources. Azure Synapse Analytics brings the two worlds of Data Analytics and Big Data Management (with data warehousing and data lakes) together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.
Though they seem pretty similar and Synapse also uses its own version of Data factory for data integration, Data Factory has more integrations and supports power query, cross-region integration runtime, and global parameters. Synapse has Spark notebooks, Spark job definitions, and SQL pool stored procedure activities that are not available in ADF. Also, Synapse does not have the SSIS package execution activity and GitHub integration.
What Is Azure Synapse Analytics?
With Azure Synapse Analytics, you can combine data integration, data exploration, data warehouse, and big data analytics into an unlimited analytics service. Using one platform, users can combine their data engineering, data science, and machine learning needs without having to manage separate tools and processes.
Utilizing the familiar SQL language, Azure Synapse allows users to query both relational and non-relational data. The data analysis and exploration can be performed using serverless on-demand queries for ad hoc data analysis and exploration or provisioned resources (dedicated SQL pool) for predictable and demanding data warehouse needs.
What is Azure Databricks?
Azure Databricks is an Apache Spark-based analytics platform built on Microsoft Azure. Based on Apache Spark, Azure Databricks is used to process large data workloads that allow collaboration between stakeholders to derive actionable insights with a one-click setup, streamlined workflows, and interactive workspace.
It is a managed platform that gives data developers all the tools and infrastructure to focus on data analytics without worrying about managing Databricks clusters, libraries, dependencies, upgrades, and other tasks unrelated to driving insights from data.
Difference between Azure Databricks and Azure Data Factory?
Azure Databricks, an Analytics platform, opens a collaborative space for Data Engineers and Data Scientists to perform ETL activities and build ML algorithms. Whereas, Azure Data Factory is primarily focused on Data integration and mapping data flows.
Azure Data Factory known for its GUI to drag and drop data features in creating pipelines helps visualize data flows visually. In contrast, Databricks uses Python, Spark, R, Java, or SQL, therefore, requires a certain amount of coding knowledge. Another difference between the two is that though ADF and Databricks support batch and streaming options for data processing, ADF doesn’t support live streaming, whereas Databricks does with its Spark API.
What is Apache Spark?
Apache Spark, the platform on which Databricks is based, is a data processing framework that can quickly perform processing tasks on very large data sets, and can also distribute data across computers with distribution tools. By using in-memory caching, optimized query execution, and batch processing Spark provides fast analytics queries for data of any size.
Spark can be deployed in a number of ways with Java, Scala, Python, R programming, SQL, Graph Processing, Machine Learning, and streaming data. Spark was created to address the limitations of Hadoop MapReduce (normally used for processing big data sets) like slow querying by processing data in memory.
Why should you migrate to Azure?
By migrating to Azure you can save up to 80% on Windows server, bring 93% more energy efficiency, spend less money (up to 5x than with AWS) and spend more time on increasing your work efficiency.
On Azure, you can get extended security updates for three more years and manage your cash flow better. By accelerating app development cycles, you can built-in scalability and build enterprise-grade solutions. With Azure, you can have added facilities like Azure Data Factory, Azure Kubernetes, Azure ML, Power BI, and many more suited to your needs.
What are the steps involved in Azure Migration?
The Azure Migration process can be defined in multiple ways.
One of the ways is in three stages: Planning, Implementation, and Operations stages where in the Planning stage you define objectives, strategy, and plan for the migration. In Implementation you ready the data, upskill staff and start the adoption. In the operations stage, you govern and manage the migration done.
Another method is in four stages, which are to Assess, Migrate, Optimize, and Manage the applications and workloads. This starts by assessing your existing applications and infrastructure to migrate them to Azure, and streamlining and optimizing your resources. Then secure and manage your resources by fine-tuning the management of resources. The steps can be further modified based on your requirements
What is Azure Data Factory?
Azure Data Factory is Azure’s ETL Cloud service that offers a code-free Graphical User Interface (GUI) for serverless data integration and data transformation. It is used to prepare data, construct ETL and ELT processes, and orchestrate and monitor pipelines code-free.
Azure Data Factory can act as the data integration layer in data transformation activities. With Azure Data Factory you can load data from many sources, transform the data, publish the data, and monitor the data flows to automate data movements with a rich UI, therefore eliminating the need to know coding languages.
Difference between Azure Data Factory and SSIS
SSIS or SQL Server Integration Services is an on-premise tool that has been the to-go ETL tool for many organizations, it comes with commercial instances of SQL server. Whereas Azure Data Factory, a serverless tool helps you design your data movements for enterprise data management.
SSIS comes pre-built with SQL server licensing and therefore doesn’t need ongoing costs and licensing fees. But the disadvantage is that it cannot connect with services like Azure Databricks, Azure Synapse, etc. and the work can only be done with SSIS. Whereas with ADF, you can connect with SSIS, Power Query, and other services along with designing both ETL and ELT flows.
What is DevOps?
In simple words, DevOps is the combination of Developers (Dev) and Operations (Ops) to create an ecosystem of better efficiency, speed, and security of software development & delivery. It combines the best of people, processes, and technologies to evolve and improve products at a faster pace.
This has been developed to ensure the quality of applications, by creating an infinite loop of delivery and feedback by using an Agile approach to software development. DevOps currently represents the change in the mindset of IT culture, which focuses on incremental changes, an agile approach, joint responsibility, improved collaboration, and reliability.
What are the phases of a DevOps pipeline?
The 8 phases in a DevOps pipeline namely: Plan, Develop, Build, Test, Release, Deploy, Operate, and Monitor. You can understand the phases better with the infographic below:
What are the benefits of using Azure DevOps?
Some of the benefits of using Microsoft Azure for your DevOps implementation are because it provides a Cloud and Platform Agnostic platform as it runs on any platform (Linux, macOS, and Windows) and language (e.g., Android, C/C++, Node.js, Python, Java, PHP, Ruby, .Net, and iOS apps). It is also compatible with AWS and GCP.
Other benefits include increased collaboration even if the only code your team has is a collection of PowerShell or VB scripts which you can store in Azure as a central repository. With an extensive marketplace for plugins and integrations, you can keep adding new IaaS features to your existing codes.
Azure Machine Learning
What are the capabilities of Azure Machine Learning?
Some of the capabilities of Azure Machine Learning are:
- Creation of ML models with interactive GUI
- Automated ML feature to run automated model experiments
- Compute options for varying machine learning workloads
- Datastores to mount data from Azure Storage services such as a data lake store
- Supports Jupyter notebooks, Jupyter Labs, Github integration, and R Studio.
- Build compute-intensive workloads
What are a few use cases of Azure Machine Learning?
The most common use cases of Azure Machine Learning are:
- Inventory optimization
- Recommendation Engine
- Sentiment Analysis
- Fraud Detection
- Demand forecasting
- Supervised learning and Supervised learning
- Churn Prediction
- Pattern recognition
Difference between Azure Databricks vs Azure Machine Learning
Azure Machine Learning Service and Azure Databricks have always been the top contenders for running advanced experiments on your data. But it is important to note that Databricks is an Apache-Spark-based Analytics service whereas Azure Machine Learning is a full-fledged advanced analytics platform.
Though there are multiple differences in the platform, the key difference lies in its usage and classification, Azure Databricks can be used as a General analytics tool whereas Azure Machine Learning is an MLaaS tool. The other differences are that though for scalability Databricks is better, Azure ML has better UI & is low-code. Databricks can be used for heavy data preparation and modeling whereas AMLS can be used for advanced analytics, deep learning, and operationalization.
Microsoft Azure comes with a myriad of features and services that can be leveraged by organizations. In case you are looking for more information on Azure and what service would be suitable for you or to have seamless Microsoft Azure implementation, get in touch with us today.