class: cloudtitle,center, middle # Overview of Big Data with Spark for For Researchers ### Pat Bills, MSU ICER, October 2023 for the MSU Cloud Computing Fellowship [Session 5](../session_big_data) --- # Why are we talking about Data and not cloud? *this is not the "Data Fellowship"* - we all have data to deal with - a common research struggle is working with data too large for your machine. - Big data tools can offer a solution - cloud computing offers Big Data tools "as a service" - without cloud these systems require a collection of hards, a month of installation, infrastructure expertise - Big data tools are becoming commonplace for data science - Potential gateway to whole new realm of solutions for parallelizing your work  *Image by Alexander Sinn on Unsplash* --- # Why are we talking about Data and not cloud? - is it applicable to every problem? no. - do you need to use big data for your project? no. - is it worth learning at least the principles. yes, similar to cloud computing - for some of your research this could be a game-changer - **Session Goal:** understand the principles, and some idea of what's involved to deploy and use --- # Ways that our data can be 'too big' - not enough storage space - doessn't fit on disk - not enough workspace - can't fit into memory - not enough time - calculations or combinatorial work too slow Doug Laney in 2001 and [Gartner 2011](https://www.gartner.com/en/information-technology/glossary/big-data) define "Big Data" as having one of - **Volume**: too much data to house or process using commodity hardware and software. - **Velocity**: new data arrive too quickly to manage or analyze or analysis - **Variety**: so much variation in data records or too many diverse sources, e.g. not rigidly structured. --- # Some solutions to the 'too big for machine' problem <img src="../img/smallbox_bigcat.jpg" width="250px" align="right"> **why couldn't I just just get a bigger box ? ** Cost for large VM does not go up linearly and these are expensive: - high memory - many-core count (e.g. use your language built-in parallel methods) - large disk systems to attach to the VM - your time to configure/setup is valuable --- # Some solutions to the 'too big for machine' problem Take smaller bites - split the data up and work with some at time - work with only the most interesting subset - use a data system such as a Relational Database Server - use a tool that combines these techniques --- # Parallel Data Analysis - dividing up work and process concurrently is the core of what these tools do - parallel techniquees are important even for storage: - storing data across hundreds of disks. - there are othre forms of parallel computing but that's beyond the scope, e.g. HPC --- # DIY Parallelism - so-called "Data parallelism" using the "split-apply-combine" strategy - labor intensive: - manage data & partitions - manage worker machines - coordinate (orchestration) - handle failures - for really BIG DATA may not be possible <img src="../img/split-apply-combine-daigram.png" width="400px"> --- # DIY Parallelism Recipe - adapt your code to process partitions - e.g. given subset of row numbers, reads in just rows andd calculate summaries - create an index of partitions (eg. each partition could have a lits of the row numbers) - create a new machine for each parallel worker you want to use, copy the software to each - for each partition - send the partition index to one of the workers, - or have the worker check which piece needs doing next - the worker reads and processes the rows it was given, then writes an output file (output01.txt) - until all partitions are processed - combine all of the output files into one complete output file --- # Big Data Tools: what's the big deal? ### innovations: - optimize data movement in network - data partitioned across machines, operate data near machine - works on "commodity hardware" not expensive HPC - open source => democratized --- # Big Data Tools Ecosystem The history of big data tools starts at Google, who published papers on thier systms, and others who create open source software projects based on those papers, an other academic groups (e.g. Amplab Stanford) The history is not important, just common "big data" terms: - "Map Reduce" algorithm publication by Jeff Dean/Google based on their distributed file system - "Hadoop" was the first implementation of MapReduce. Still widely used in businesses - "HDFS" cluster file system invented with Hadoop, and required for Hadoop to use - "Apache Spark" project general purpose, not limited to MapReduce, doesn't require HDFS, multi-language --- # Spark : "Unified analytics engine" - general purpose parallel storage and execution system - within a Spark system you set up data sources, clusters of computing, and run special versions of libraries designed for parallel exectution - can work with several storage systems including cloud storage - you provide the code, Spark evaluates and forms a parallel execution plan - automatically partitions the data, and can automatically - code can be R, Python, SQL, or Scala (a version of Java) - you can interact using Notebook, or with a remote connection <br> *Sorting distributed data with Apache Spark, from Mastering Spark with R* --- # DataBricks == Spark "Pro" - Databricks **company** started by Spark inventors - Databricks **product** packages Spark with robust web interface and connections - Azure Databricks **service** easy to deploy databricks with all details taken care of for you - there are similar versions for AWS and Google Cloud - Has a really convenient web and notebook interface for immediate and interactive cluster computing - To use Spark, you first need to build a cluster of computers and install it - Databricks is parent service that runs a web interface from you which you can create clusters on demand - Takes care of creating & configuring all Azure resources --- # Databricks Introduction Video Two videos linked Link to Video introduction and walk through from Doug Krum, MSU IT Services Data Architect in our session  --- # Notes on Using Databricks - Designed for one Databricks for many users: - an IT Administrator sets up the service for users to log-in and use (data analysts) could log-in and use the service - we can still use it on as a single researcher for interactive work - This is the first "platform as a service" system - all resources are created and destroy automatically for you - creates a special resource group Databricks uses to put resources - if you are not use it for computation, there are no VMs & Disks hanging around --- # Do you really _need_ a big data tool? #### Big data tools can work magic on huge datasets, but require: - significant resources to build and to start-up a cluster. e.g.'overhead' - learning a new method of programming to take advantage of their parallel functions. #### Other options for [Azure Analytics](https://azure.microsoft.com/en-us/product-categories/analytics/): - relational database managment systems (RDBMS) were designed to proceess millions of rows of tabular data in seconds with very small memory. - Azure offers several open source RDBMS systems - There are database 'engines' you can use on your laptop without the cloud, specifically SQLite and DuckDB, both possible to use in R and Python (and many other languages. ) - Requires learning SQL and adding code to connect or use the database in your workflow - VM 'scale sets' : launch dozens of identical VMS from an image - Azure Batch: build a pool of works from a container - has a management tool called "ship yard" - could build and run a DIY spark cluster on this - HPC in the cloud : cycle cloud - Azure "cosmos db" - you pay by the query, it's already setup and ready to process data` --- # Recap <img src="https://res.cloudinary.com/hevo/image/upload/f_auto,q_auto/v1613111770/hevo-learn/spark_logo.png" width="100" align="right"> <img src="data:image/svg+xml;base64,<svg width="132" height="22" viewBox="0 0 132 22" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M18.3176 9.27479L9.68663 14.1339L0.444511 8.94223L0 9.18241V12.9515L9.68663 18.3833L18.3176 13.5427V15.5381L9.68663 20.3972L0.444511 15.2055L0 15.4457V16.0923L9.68663 21.5242L19.3547 16.0923V12.3233L18.9102 12.0831L9.68663 17.2563L1.03719 12.4157V10.4203L9.68663 15.2609L19.3547 9.82906V6.11545L18.8732 5.83831L9.68663 10.993L1.4817 6.41106L9.68663 1.81061L16.4284 5.59813L17.0211 5.26557V4.80367L9.68663 0.683594L0 6.11545V6.70667L9.68663 12.1385L18.3176 7.27942V9.27479Z" fill="#EE3D2C"></path><path d="M37.449 18.4427V1.85156H34.8931V8.0594C34.8931 8.15177 34.8375 8.22568 34.7449 8.26263C34.6523 8.29958 34.5597 8.26263 34.5041 8.2072C33.6336 7.19104 32.2816 6.61829 30.7999 6.61829C27.6327 6.61829 25.1509 9.27879 25.1509 12.6783C25.1509 14.3411 25.725 15.8746 26.7808 17.0016C27.8365 18.1287 29.2626 18.7383 30.7999 18.7383C32.2631 18.7383 33.6151 18.1287 34.5041 17.0755C34.5597 17.0016 34.6708 16.9832 34.7449 17.0016C34.8375 17.0386 34.8931 17.1125 34.8931 17.2049V18.4427H37.449ZM31.3555 16.4289C29.3182 16.4289 27.7253 14.7845 27.7253 12.6783C27.7253 10.5721 29.3182 8.92775 31.3555 8.92775C33.3928 8.92775 34.9857 10.5721 34.9857 12.6783C34.9857 14.7845 33.3928 16.4289 31.3555 16.4289Z" fill="black"></path><path d="M51.118 18.4435V6.8962H48.5806V8.06017C48.5806 8.15254 48.525 8.22645 48.4324 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12.6783Z" fill="black"></path><path d="M94.4766 9.26328C94.7173 9.26328 94.9396 9.28175 95.0878 9.3187V6.69515C94.9951 6.67668 94.8285 6.6582 94.6618 6.6582C93.3282 6.6582 92.1058 7.3418 91.4576 8.43187C91.402 8.52425 91.3094 8.5612 91.2168 8.52425C91.1242 8.50577 91.0501 8.41339 91.0501 8.32102V6.89839H88.5127V18.4642H91.0686V13.3649C91.0686 10.8337 92.3651 9.26328 94.4766 9.26328Z" fill="black"></path><path d="M99.2917 6.89746H96.6987V18.4632H99.2917V6.89746Z" fill="black"></path><path d="M97.9576 1.87012C97.0871 1.87012 96.3833 2.57219 96.3833 3.44055C96.3833 4.30891 97.0871 5.01099 97.9576 5.01099C98.8281 5.01099 99.5319 4.30891 99.5319 3.44055C99.5319 2.57219 98.8281 1.87012 97.9576 1.87012Z" fill="black"></path><path d="M106.886 6.60059C103.33 6.60059 100.755 9.15023 100.755 12.6791C100.755 14.3973 101.367 15.9308 102.459 17.0394C103.571 18.1479 105.126 18.7576 106.867 18.7576C108.312 18.7576 109.423 18.4805 111.535 16.9285L110.072 15.395C109.034 16.0786 108.071 16.4112 107.127 16.4112C104.978 16.4112 103.367 14.8038 103.367 12.6791C103.367 10.5544 104.978 8.947 107.127 8.947C108.145 8.947 109.09 9.27956 110.035 9.96316L111.664 8.42968C109.757 6.80382 108.034 6.60059 106.886 6.60059Z" fill="black"></path><path d="M116.035 13.362C116.072 13.325 116.128 13.3066 116.184 13.3066H116.202C116.258 13.3066 116.313 13.3435 116.369 13.3805L120.462 18.4428H123.611L118.314 12.0502C118.239 11.9578 118.239 11.8285 118.332 11.7546L123.203 6.89551H120.073L115.869 11.108C115.813 11.1634 115.721 11.1819 115.628 11.1634C115.554 11.1264 115.498 11.0525 115.498 10.9602V1.87012H112.924V18.4613H115.48V13.9532C115.48 13.8978 115.498 13.8239 115.554 13.7869L116.035 13.362Z" fill="black"></path><path d="M127.776 18.739C129.869 18.739 131.999 17.4642 131.999 15.0439C131.999 13.455 130.999 12.3649 128.962 11.6998L127.572 11.2379C126.628 10.9238 126.183 10.4804 126.183 9.8707C126.183 9.16863 126.813 8.68826 127.702 8.68826C128.554 8.68826 129.313 9.24253 129.795 10.2033L131.851 9.09473C131.092 7.54277 129.517 6.58203 127.702 6.58203C125.405 6.58203 123.739 8.06009 123.739 10.0739C123.739 11.6813 124.702 12.7529 126.683 13.3811L128.11 13.843C129.11 14.1571 129.536 14.5635 129.536 15.2102C129.536 16.1894 128.628 16.5404 127.85 16.5404C126.813 16.5404 125.887 15.8753 125.443 14.7852L123.35 15.8938C124.035 17.649 125.72 18.739 127.776 18.739Z" fill="black"></path><path d="M58.2304 18.628C59.0453 18.628 59.7677 18.5541 60.1751 18.4986V16.2815C59.8418 16.3185 59.2491 16.3554 58.8972 16.3554C57.86 16.3554 57.0636 16.1707 57.0636 13.9351V9.18687C57.0636 9.05754 57.1562 8.96517 57.2858 8.96517H59.7862V6.87741H57.2858C57.1562 6.87741 57.0636 6.78503 57.0636 6.6557V3.33008H54.5076V6.67418C54.5076 6.80351 54.415 6.89589 54.2854 6.89589H52.5073V8.98364H54.2854C54.415 8.98364 54.5076 9.07602 54.5076 9.20535V14.5818C54.5076 18.628 57.2117 18.628 58.2304 18.628Z" fill="black"></path></svg>" align="right"> - Big Data is an attempt to solve the problem of analyzing data bigger than one machine by harnessing a 'cluster' of machines to parallelize storage and processing of huge datasets across the 'workers' (machines) in a cluster. In the cloud these are VMs. Note this is not the same as an HPC cluster. - Spark is an open source 'big data' system for creating and using clusters to analyze data with different programming languages (Python, R, SQL and "scala" which is a form of the Java language). Spark has a library of special functions to automatically parallelize (split up) the work on the cluster for you. Spark was invented at Stanford and is an open source product: https://spark.apache.org - The Spark inventors created a company and product "DataBricks" that adds features and ease of use: https://www.databricks.com/spark/comparing-databricks-to-apache-spark - Both AWS and Azure have a database service that is nearly identical to run a databricks workspace and cluster very easily, providing a single platform for databricks and other cloud services, and one point of billing and contract (essential for private companies). Otherwise we'd need a data center and IT staff to create a databricks system. - there are many options for analyzing data in the cloud that don't have "big" in the name but are nonetheless amazing - Cloud computing makes big data accessible to all of us. --- # Questions?  ---