Paxata is built on Apache Spark to deliver big data scale and performance. Many data practitioners tend to rely on code or scripts which are error-prone and require technical expertise. In his former role as a Senior Solutions Consultant, he worked with several prospective customers and partners to demonstrate the value Paxata can provide to their organizations. The play-by-play (PBP) data came from NBAStuffer. For these analysis, we have a few days to turn this around, and we want to be able to spin-up to larger machines if we have to. Xcalar is a cloud-scale platform for data and analytics applications. The power of Analytics, “Big data” and the NBA.
The weightings were then applied to the criteria via a SQL statement (below), Parameters are indicated by
Prior to joining Paxata in 2014, he worked for Ernst & Young as an enterprise analytics consultant where he managed teams focused on risk and compliance analytics and business performance reporting for clients in the high tech, media & entertainment, and healthcare industries. Paxata’s intelligent parser automatically converts and flattens the JSON file into a tabular structure, allowing subsequent profiling and blending with other datasets within a data prep project. ! Deployed in the cloud for speed, performance and operationalization. This data arrives in near real-time and must be parsed, filtered, and transformed to feed various statistical models in support of coaching and roster decisions. After each game/evening, new PBP data is also uploaded into this S3 bucket.
This website uses cookies to improve your experience while you navigate through the website. Thanks! Its serverless environment for data engineers and analysts enables data transformation and deployment of ML models. Out of these cookies, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. With a visual, Excel-like interface to interact with data in real time, Paxata guides the user to explore, clean, standardize, append, and join data using simple clicks. Call: 1-855-9-PAXATA Join us as we celebrate Spreadsheet Day by giving Excel the day off—at least for the data prep portion of our workflows. The rise of big data is pitting the old school against the new school as the NBA undergoes its analytics revolution.
All about Power BI, Modelling, DAX and Microsoft BI. Manipulating a date column (aka remember time intelligence functions), The power of Analytics, “Big data” and the NBA. With Xcalar, a data engineer can seamlessly integrate SQL, Python and visual modeling nodes all in a single data flow. Embedded algorithms profile data on an ongoing basis and provide recommendations for standardizing, cleaning, appending, and joining data. So once we got all 24 criteria generated across 3 dataflows (offense, defense, championship), we then wanted to put in weightings, as all criteria are not evaluated equally. We'll reach out and get your NBA instance setup straight away! After last night's games PBP, we have 2.8 GB data representing 9 million records. Like our previous analytics deconstructions, our architecture resides in the cloud - AWS to be precise. If you spent any time looking at pre-game analysis, shot charts, or player vs. player matchups before the 2019 NBA Finals between the Toronto Raptors and the Golden State Warriors, you have likely benefitted from the player and ball tracking technology made possible by, .
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