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Showing posts with the label #BigData

downloading folders from google drive.

I wanted to download some course material on RL shared by the author via Google drive using the command line.  I got a bunch of stuff using wget a folder in google drive was a challenge. I looked it up in SO which gave me a hint but no solution. I installed gdown using pip and then used: gdown --folder --continue https://drive.google.com/drive/folders/1V9jAShWpccLvByv5S1DuOzo6GVvzd4LV if there are more than 50 files you need to use --remaining-ok and only get the first 50. In such a case its best to download using the folder using the UI and decompress locally. Decompressing from the command line created errors related to unicode but using the mac UI I decompressed without a glitch.
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Streaming events to BigQuery

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Here are my notes from "Streaming events with Kafka to BigQuery and Logging" Meeting of Big Things Meetup which took place at Poalim Tech Offices. The work space is quite amazing and there were many people working as late as 10 PM. Poalim Tech has is a great host for medium sized meetups. On the left, the obligatory pizza overdose BigQuery - a serverless analytics warehouse is the destination for the data. Google BigQuery in brief BigQuery is Google's Analytics serverless database  solution based on colosul and providing as yet unmatched scaling capabilities. Usage cost are typically 5 USD per TB processed. Pros are:  serverless data warehouse solution. a powerful command line interface. an SQL based interface with noSql performance. Good code examples. Cons are:  that queries can eat up many thousands of USD of compute time. clunky web interface. Apache Kafka in brief Kafka is used to build  streaming data pipelines ...
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Location: PoalimTech Offices
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Serverless Big Data

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I was at the first meeting of the p of the Big Data Analytics meetup. The first speaker was Avi Zloof CEO of EvaluteX who gave a talk titled "Serveless Big Data The Good, and the Great" The Speaker Avi Zloof EvaluateX which is located at " The Junction " in (Rothschild 9 Tel Aviv) is an outfit that has a chrome plugin which can optimize Google BigQuery SQL queries in the web interface. My Last BigQuery project however had abandoned the web interface and switched to 100% automation via the API. Also despite having massive queries there was little need to optimize them. I had been more concerned with comparing different editions of the projects to detect data discrepancies. The Big Data and GUI connection is often the primary challenge however this was not the subject of the talk. The talk introduced me to EvaluateX and their activity. Mr. Zloof shared many interesting professional insights as well as his point of view regarding serverless database platfor...
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downloading folders from google drive.

I wanted to download some course material on RL shared by the author via Google drive using the command line.  I got a bunch of stuff using wget a folder in google drive was a challenge. I looked it up in SO which gave me a hint but no solution. I installed gdown using pip and then used: gdown --folder --continue https://drive.google.com/drive/folders/1V9jAShWpccLvByv5S1DuOzo6GVvzd4LV if there are more than 50 files you need to use --remaining-ok and only get the first 50. In such a case its best to download using the folder using the UI and decompress locally. Decompressing from the command line created errors related to unicode but using the mac UI I decompressed without a glitch.
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Random Thoughts on Linear Regressions

Regression Analysis TLDR Regression is the oldest and most powerful tool in a data scientist's toolbox. Under ideal conditions multiple linear regression would be the best and only tool a data scientist would want to use... In reality you would use a modern nonparametric variant or a different algorithm. Still, the main ideas I . discuss here  will pop up in many other models and algorithms. This post is my brain dump on regression - I'll update it as time allows to cover the many aspects of this technique.
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SQL - Selection v.s. Projection

Selection and projection are two high level processes taking place when SQL queries are executed. Selection is choosing some records (rows) from a table and leaving others out. e.g. rows having name='oren'. Projection is the choosing some columns from each record and leaving others out. e.g. only name. So the select keyword performs projection while the where and keyword performs selection. Clearly the choice of using the keyword select for projection (choosing columns) rather than choosing rows, is an unfortunate flaw in the design of SQL, but this oversight is too well established to be fixed.
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