As data teams scale, data pipelines become increasingly interconnected and often share components. Though efficient for development, upstream changes can cause unintended consequences to downstream datasets. In this talk, we’ll show how data validation solves this and especially focus on how to scale current validation frameworks to handle big data with Spark and Dask.
Data validation is implementing checks to see if data is coming in (and being processed) as expected. Data teams apply data validation to preserve the integrity of existing data workflows. As data pipelines become interconnected, it becomes very easy for one pipeline’s changes to cause breaking changes to other data applications. In situations like this, data validation serves both as tests for the pipeline, and as a monitoring solution to capture malformed data from flowing through the system. Without these checks, data applications can produce inaccurate results without anyone being alerted.
While data validation frameworks are available, it is still hard to bring these solutions to big data. Most frameworks are built for pandas and are challenging to apply with distributed compute frameworks such as Spark and Dask, if at all possible. In this talk, we will cover the basics of data validation, but more importantly, we will also discuss how to apply it on a large dataset.
To do this, we will use Fugue, an abstraction layer that enables users to port pandas, Python, and SQL code to Spark and Dask. By combining Fugue with existing validation frameworks such as Pandera, we can port pandas-based validation code and apply it distributedly. For large scale data, there is also a unique use case to apply different validations on different partitions of data. This is currently not feasible with any single validation library. In this talk, we will show how validation by partition can be achieved by combining Fugue and validation frameworks such as Pandera.
#bigdata #spark #dask
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.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
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.
#big data analytics service providers #top big data analytics companies #impact of big data on businesses #best big data consulting firms #big data #big data for businesses
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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