Cloud Data Technologies – Technology selection is an important part of the design and architecture process for building a data analytics solution. The tools chosen must be responsive to business needs. Therefore, it is essential to spend time understanding these requirements in depth, as well as the business rules in your organization. The result should be a set of technologies and tools that are well integrated into a system to meet business needs and not the other way around. If users of the system must adapt to vehicle limitations, the choices made during the design phase may not always be optimal. This is not an easy task as it is not a perfect tool. You need to consider the advantages and disadvantages of each tool when making your decision, it is very helpful to look at the situation and workflow and choose only those that fit your organization’s practices, regulations and more. To help you get started, here we share the ultimate list of technologies and platforms that enable data analytics. Download our exclusive whitepaper for a guide to building your data analytics solution.
Every system needs a platform to run on. A very popular option today is to deploy your system in the cloud. Having a large and complex system in the past meant you had to build your own data center with your own physical machines, which is no longer the case today. Cloud platforms enable you to deploy and operate large and complex systems on data centers, infrastructure and services provided and maintained by cloud service vendors. This comes with some benefits, including the ability to scale parts of your architecture in a few clicks as demand changes, rather than adding more physical machines to your hub.
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Microsoft Azure is Microsoft’s public cloud computing platform. It provides a range of cloud services, including computing, analytics, storage, and networking. Users can choose from these services to develop and scale new applications or run existing applications in the public cloud.
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The Azure platform aims to help businesses manage challenges and achieve their corporate goals. It offers tools that support all industries, including e-commerce, finance, and various Fortune 500 companies. It is also compatible with open source technologies, giving users the flexibility to use the tools and technologies they prefer. Additionally, Azure offers 4 different types of cloud computing: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), and Serverless.
Microsoft charges for Azure services on a pay-as-you-go basis; this means that customers receive a monthly invoice for the specific resources they use.
Amazon Web Services (AWS) consists of several different cloud computing products and services offering server, storage, networking, remote computing, email, mobile development, and security. AWS can be divided into three main products: Amazon’s virtual machine service EC2, Glacier, a low-cost cloud storage service, and Amazon’s storage system S3. As of February 2020, an independent analyst reported that AWS owns more than a third of the market at 32.4%.
AWS has 76 Availability Zones where its servers are located. These serviced areas are divided to allow users to set geofences on their services (if they wish) and to provide security by diversifying physical locations. In total, AWS is spread across more than 245 countries and regions.
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Google Cloud Platform is a provider of computing resources for deploying and managing applications on the web. Its expertise is to provide a place for individuals and businesses to build and run software and uses the Web to connect with users of that software. Google Cloud consists of a set of physical assets such as computers and hard disk drives located in Google’s data centers around the world, and virtual resources, including virtual machines (VMs). Each data center location is in a region Regions in Asia, Australia, Europe, North America, and South America This distribution of resources provides several benefits, such as failover redundancy and reducing latency by locating resources closer to clients. This distribution also introduces some rules regarding the use and combination of resources.
Data integration is an essential part of a big data analytics solution as it allows the integration of multiple systems into a single data model that is then used to gain insights and insights. In other words, it’s the way you present data to your analytics system. Every data analytics system needs data to be analyzed, and this is the area that governs its system. There are many different approaches to achieving this goal, all different and therefore solving different problems. A commonly used method is ETL (External, Convert Payload). ETL is a process by which large amounts of required data are extracted from various data sources and converted into a common format defined by your organization’s data model. The data is then cleaned and loaded into private storage such as a data warehouse or data lake. It can then be used for standard reporting and analysis purposes. Regardless of the deployment method you choose (cloud or on-premises), your system needs a data integration.
Microsoft SQL Server Integration Services (SSIS) is a platform for building enterprise-grade data integration and data transformation solutions. Use Integration Services to solve complex business problems by copying or downloading files, installing data warehouses, cleaning and examining data, and managing SQL Server objects and data.
Integration Services can extract and transform data from a variety of sources, such as XML data files, flat files, and relational data sources, and then load the data into one or more destinations.
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Integration Services includes a rich set of built-in tasks and transformations, graphical tools for creating packages, and the Integration Services Catalog database where you store, run, and manage packages.
You can use graphical Integration Services tools to build solutions without writing a single line of code. You can programmatically package and code custom tasks and other package objects to programmatically integrate the Integration Services object model.
Azure Data Factory is Azure’s cloud ETL service for scale-out serverless data integration and data transformation. It offers intuitive scripting and a code-free user interface for one-window monitoring and management. You can take existing SSIS packages and migrate them to Azure and run them in ADF with full compatibility. SSIS Integration Runtime provides a fully managed service so you don’t have to worry about infrastructure management.
Xplenty is an ETL platform that requires no coding or deployment. It has a point-and-click interface that enables simple data integration, processing, and provisioning. It also connects to various data sources and has all the capabilities you need to perform data analytics.
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AWS Glue is a fully managed export, transform, and load (ETL) service that makes it easy for customers to prepare and upload their data for analytics. You can create and run an ETL job in a few clicks in the AWS Management Console. Simply point AWS Glue to the data stored in AWS, and AWS Glue discovers your data and stores associated metadata (such as table definitions and schemas) in the AWS Glue data catalog. Once cataloged, your data is instantly searchable, shuffled and available for ETL
Cloud Data Fusion is a fully managed, cloud-native enterprise data integration service provided by Google for quickly creating and managing pipelines.
Cloud Data Fusion Web UI lets you build scalable data integration solutions to clean, prepare, consolidate, transfer and transform data without managing infrastructure. Cloud Data Fusion is powered by open source project CDAP
Even properly stored legacy data can provide value for new analytical tools. Fortunately, data storage is more affordable than ever and will continue to be so in the near future.
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Microsoft SQL Server is a relational database management system or RDBMS developed and marketed by Microsoft. Like other RDBMS software, SQL Server is built on SQL, a standard programming language for interacting with Reliance databases. SQL Server depends on Transact-SQL or T-SQL, Microsoft’s SQL implementation that adds a set of custom programming constructs. SQL Server can be used to create a fully functional data warehouse
Azure SQL Data Warehouse (SQL DW) is a petabyte-scale MPP analytical data warehouse built on SQL Server and running as part of the Microsoft Azure cloud computing platform. Unlike other cloud MPP solutions, SQL DW separates storage and compute and bills for each separately. Like many other analytical data warehouse solutions, SQL DW encompasses physical machines and represents computing power in the form of data warehouse units (DWUs). It allows users to calculate resources effortlessly and easily.
Azure SQL Data Warehouse is part of the Microsoft Azure cloud computing platform, making this database a virtual, easy choice for companies that have already invested in the Microsoft technology stack.
Azure Data Lake includes all the capabilities developers, data scientists, and analysts need to store data of any size, shape, and movement and perform any task.
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