This is part 3 of a series on data engineering in a big data environment. It will reflect my personal journey of lessons learnt and culminate in the open source tool Flowman I created to take the burden of reimplementing all the boiler plate code over and over again in a couple of projects.
This series is about building data pipelines with Apache Spark for batch processing. But some aspects are also valid for other frameworks or for stream processing. Eventually I will introduce Flowman, an Apache Spark based application that simplifies the implementation of data pipelines for batch processing.
Part 1 of the series already pointed out the existence of two types of requirements, which apply to probably almost all kinds of applications: Functional and Non-Functional requirements. Let us first focus on the first type of requirements.
Functional requirements describe the real problem that a solution should solve in the first place. They describe the core functionality and what should be implemented. That can be a data processing application which is required to integrate multiple data sources and perform an aggregation to provide a simplified data model for some data scientists. But the idea of functional requirements also apply to an Android application for booking a trip to different city.
Functional requirements are always written by business experts as proxies of final end user. They should focus on the problem to be solved, such that the developer or architect still can decide how a viable solution should look like. Of course the chosen approach should be validated together with the business experts before implementation.
Note that I specifically wrote that functional requirements should focus on the problem (or task) and not on the solution. This small difference is very important, since it leaves up a bigger design space for the developer to find the best possible solution as opposed to a specific solution given by the business expert, which may be difficult to implement within the given technology.
#big-data #spark #data-engineering
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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Traditional data processing application has limitations of its own in terms of processing the large chunk of complex data and this is where the big data processing application comes into play. Big data processing app can easily process complex and large information with their advanced capabilities.
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Big Data has played a major role in defining the expansion of businesses of all kinds as it helps the companies to understand their audience and devise their business techniques in accordance with the requirement.
The importance of ‘Data’ has been spoken very highly in the modern-day business. Thus, while using big data analysis, the companies must keep away from these minor mistakes otherwise it could have a major impact on their performances. Big Data analysis can be the silver bullet that can answer your questions and help your business to scale newer heights.
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The rapid growth of technology has led to many people opting for online services, and thus the collection and maintenance of data becomes a significant factor for any company. Big data analytics service providers can help the companies get a massive edge over their competitors as they would manage the data well and allow the businesses to make better business decisions. It will provide you with a combination of increased customer experience, revenue, and reduced cost and thus will create a win-win situation for your business. Big data technologies will be your perfect ally in excelling in the cut-throat business environment and come out with flying colors.
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An extensively researched list of top Microsoft big data analytics and solution with ratings & reviews to help find the best Microsoft big data solutions development companies around the world.
An exclusive list of Microsoft Big Data consulting and solution providers, after examining various factors of expert big data analytics firms and found the equivalent matches that boast the ace qualities with proven fineness in data analytics. For business growth and enterprise acceleration getting inputs from the whole data of the organization have become necessary, thus we bring to you the most trustworthy Microsoft Big Data consultants and solutions providers for your assistance.
Let’s take a look at the List of Best Microsoft big data solutions Companies.
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