In order for SMEs - and larger enterprises - to both compete and succeed in the global ecosystem of the current market, it is a requisite for these enterprises to utilize advanced technological systems. Modern businesses and enterprises of all sizes largely rely on technology to carry out their daily operations, to optimize workflows, and to ultimately fulfill their overarching organization’s business goals. As technology rapidly evolves, businesses have the opportunity to leverage more advanced systems to better their business operations and, ultimately, increase their bottom and top lines. There are typically five core components of a business Information Technology (IT) infrastructure: hardware, software, enterprise suites, networking systems, and database systems. Regarding the latter, database systems have always been a crucial part of a company’s operations, as everything from sales data, to private customer information, to business logs has been recorded, parsed, collated, categorized, and later queried and/or analyzed to produce actionable insights via reports - all from raw data. Typically, raw data was stored in either relational or non-relational database systems, and - regarding the former - Relational DataBase Management Systems (RDBMS) became a standard, in conjunction with Structured Query Language (SQL) - the language of choice for querying, manipulating and transforming data in a database. Usually, business database systems were kept on premises in private data-servers.
As businesses worldwide have adapted to a globalized world, and adopted evolving technology to further their enterprise, the traditional business analyst and/or business advisor gave way to data scientists. Data science has paved the way for utilizing large amounts of raw business data (Big Data) in order to turn them into actionable insights, or Business Intelligence (typically in the form of reports, etc.) - all via careful collection, parsing, manipulation, querying and analysis of very large data sets. With the advent of Big Data and advanced technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT), more complex methods of real-time data collection/analysis, and more advanced, automated analysis of large data sets, etc. are possible, giving enterprises the unprecedented ability to make informed decisions (in real-time) that are holistic and strategic.
Along with new and evolving Big Data technologies are Cloud computing technologies, which allow public and private cloud servers to be utilized for off-premise data storage, cloud application computing, Big Data analysis, and even application development and virtualization workflows. Focusing on data storage, public and hybrid cloud systems give companies the ability to leverage larger storage systems to house their Big Data, and to analyze that data, and/or transfer that data to private, on-premise servers. Today, businesses often use Data Warehouses (DWs), also known as Enterprise Data Warehouse (EDWs). Essentially, Enterprise Data Warehouses are large, centralized and integrated data systems that are used for reporting and data analysis, making them core components of Big Data and Business Intelligence workflows. The data in EDWs are always from distinct and separate sources, but are housed in a centralized hub. This centralized, integrated format allows for comprehensive, business-wide data analysis and complex queries, in order to produce actionable insights that are pertinent and practical to a business. Many querying operations can be used with such large amounts of raw data, including manipulating, transforming, moving, and analyzing the data. Regarding those crucial functions related to an enterprise’s Big Data, moving and transforming the data is a complex process that usually utilizes the ETL function - Extract, Transform, Load.
One of the most powerful, robust and flexible cloud systems associated with Big Data storage, analysis and movement/transformation amongst EDWs, is Microsoft Azure. Microsoft Azure is an expanding, robust, managed cloud system for building, deploying, testing, and managing both applications and data, through Microsoft’s data centers. As an IaaS and PaaS (infrastructure-as-a-service and platform-as-a-service, respectively), Azure is a powerful solution that businesses can use to offset their on-premises computing resources, in order to leverage robust cloud solutions for data storage and data analysis.
As noted by RedPixie, Microsoft Azure has many benefits with regard to Big Data storage, data analysis and the production of actionable Business Intelligence (BI):
- Fast operations
- Allows for quick development lifecycles
- Highly scalable
- Comes with a robust IDE for development
- Fully integrated delivery pipeline
- Highly secure system
Utilizing Azure, companies are able to use Microsoft’s data centers as a data warehouse (to store their Big Data), while leveraging cloud computing systems for data analysis, data transformation, and the production of Business Intelligence (actionable insights and reports, etc.). Since Azure is a suite of different cloud-based tools and services, when it comes to a company’s Big Data/Business Intelligence, two crucial Azure services that can be leveraged are Azure SQL Data Warehouse (or, alternatively, Azure SQL Database), and Azure Data Factory (DF):
- Azure SQL Data Warehouse: A cloud-based, SQL-based Enterprise Data Warehouse that utilizes Massively Parallel Processing, and ultimately forms the backbone of any Azure-based Big Data solution with an EDW as its foundation. Uses PolyBase for complex data analysis and querying.
- Azure Data Factory: A cloud-based, managed service that provides complex data integration, transformation, migration, and automation workflows, including both ETL and ELT functions. Allows an enterprise to automate complex data integration workflows to combine on-premise data with large data sets in off-premise (e.g. cloud) data centers, or vice versa.
Along with this are technologies such as Hadoop and Spark (Big Data frameworks for complex data analysis), and complex algorithms that can be applied to large data sets, to carry out an unprecedented level of data analysis, the magnitude of which can greatly change the way businesses operate. Everything from Point-of-Sales (PoS) data, to social media logs, to e-commerce reports can be collected and analyzed in real-time to produce comprehensive reports and actionable insights that business leaders can use to make more informed, strategic decisions.
Despite Microsoft Azure being a robust set of enterprise solutions, when it comes to Big data/data workflow migration, transformation, integration and automation, Azure Data Factory, specifically, is a game changer that can greatly enhance the way companies handle data science and their respective operations.
1. Can be Used in The Cloud or On-Premise
The first step to utilizing Big Data to produce actionable insights is to collect the raw data and store it in on-premise servers or off-premise data centers. One of the most core functions of Azure Data Factory is the ability to integrate data between both types of data centers - on premise and off premise. That is, Azure Data Factory can be used via the cloud or via on-premise data centers (locally, using internal data centers/systems).
The way businesses collect data is changing, as many businesses have large amounts of their private data in public cloud systems. Either way, it is fully possible for an enterprise to have the freedom to choose between managing data in the cloud, or on their own on-premise servers.
Data Integration in The Cloud
The major advantage of utilizing cloud data systems is the ability to increase collaboration and data sharing within a company, where private company data can be securely accessed from any location where an Internet connection is available. Additionally, there is no need for on-premise maintenance of cloud data servers.
Move Data Collected in the Cloud to an On-Premise Server
Despite utilizing a cloud system that is off-premise, data - once moved and loaded onto local servers - can still be managed, analyzed, reported, and parsed locally amongst local, on-premise data centers. Thus, one of the most important functions of Azure DF is its ability to carry out complex data integration operations. Additionally, data can be automatically captured and migrated/transformed onto on-premise data servers, and then fully analyzed automatically via DF’s automation functionalities.
2. Streamlined Ways to Build and Automate Data Pipelines
Azure DF allows companies to automate complex data workflows and operations, without requiring complex code and development work. Azure DF allows enterprises to build and manage data pipelines using a graphical user interface (GUI), and thus streamlines the process of building, executing and triggering data pipelines that can greatly help a company’s productivity.
For instance, if a company wants to collect customer data in the cloud via a data pipeline, combine it with logs that are stored locally in on-premise servers via another pipeline, and automate the process of analyzing the data and building a report, Azure DF can automate the entire process without requiring code. Additionally, because Azure DF comes with enterprise-level security, it allows companies to feasibly automate (or manually carry out) all of their data manipulation operations while complying with the pertinent data security legislations.
3. The Ability to Transform Data and Integrate with Your BI Tools
Familiar and powerful Business Intelligence (BI) tools can be fully integrated not only with Azure Data Factory, but with the tools that are linked to Azure DF, allowing data analysts to not only transform and migrate data between data centers, but to analyze the data in order to produce actionable insights and BI reports.
For instance, Microsoft’s Power BI toolset can be integrated with Azure services (including Azure Data Factory), to provide a business with real-time reports based on inputted data, all via its powerful analytics system. Power BI, specifically, has many connections with Microsoft Azure that are available, and Azure services has hundreds of other connectors that allow businesses to utilize the best, most robust modern tools to create reports and pertinent, practical insights for their operations.
Regarding sales and marketing data operations in a company, Azure DF is able to produce and utilize rich data as well as raw data, which can be fed into BI tools to produce practical and comprehensive BI reports.
4. Access to Powerful Linked Services
Azure DF is able to become linked/connected with many other data frameworks, platforms, and software suites, giving data analysts the ability to further expand the functionalities of Azure DF to fully produce actionable insights from raw and rich data. Three of the core systems that Azure DF can become linked with is Azure Storage, Azure HDInsight, and Azure Machine Learning, all of which give companies the ability to store more data in the cloud, analyze the data with complex algorithms and data frameworks, and use technologies, such as Machine Learning, to produce the best actionable insights possible.
Azure Storage is a powerful, secure, managed, durable and scalable cloud storage solution, one that gives companies the ability to store many types of data. While Azure SQL Data Warehouse is one option of data storage, Azure Storage can also be leveraged along with Azure Data Factory to provide a complete data storage and data integration/automation solution.
Azure HDInsight is a managed cloud service that combines Big Data frameworks (i.e. Hadoop/Spark) with Microsoft Azure’s services, including Azure DF. Essentially, Azure HDInsight allows companies to process and analyze massive amounts of data in order to produce actionable insights, including data associated with Azure Storage and Azure Data Factory. This gives companies a complete Big Data analysis/BI and data storage/integration solution.
Azure Machine Learning
Machine learning is a branch of Artificial Intelligence (AI) where programs are able to “learn” and produce new insights based on their experiences (previous data processing), all without being explicitly programmed to carry out such novel analytical operations. Microsoft’s Azure ML enables companies to leverage the power of ML analytics and have robust AI systems analyze their Big Data, which can produce the most comprehensive BI possible. This allows companies to analyze large data sets at speeds and in novel ways that are unprecedented.
5. Data Factory Has Made Accessing Data faster Than Ever Before
Azure Data Factory is a fast solution when it comes to accessing data to carry out data operations and gain insights in real-time. Azure DF is fast with regards to deployment, operation, and scalability . With properly built data pipelines, companies can now access data within seconds of receiving it. Rather than using historical data, businesses can practically use real-time data.
How We Collect and Process Data is Changing How Businesses Operate
How businesses collect and use data is changing rapidly. Azure Data Factory, coupled with other Azure solutions, is giving businesses an edge when it comes to data integration, transformation, migration, analysis, and automation workflows. In conjunction with robust cloud computing suites, on and off-premise cloud storage data centers, and analytics frameworks, companies now have the ability to leverage a variety of Azure services (including AI systems) to integrate and automate complex data operations and gain new insights - in real time - that can greatly boost their productivity and increase their bottom line.