The Evolution of Big Data Platforms and Data Science
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1 IBM Analytics The Evolution of Big Data Platforms and Data Science ECC Conference 2016 Brandon MacKenzie June 13, IBM Corporation
2 Hello, I m Brandon MacKenzie. I work at IBM. Data Science - Offering Management & Worldwide Technical Sales Advisory Member - Spark Technology Center Advisory Council Co-author - IBM Apache Spark Professional Certification Program MSc - University of Edinburgh 2
3 Data volume, variety, and velocity change but the goal of the Data Science remains the same Deliver and deploy model Understand problem and domain Communicate results Ingest data Evaluate Explore and understand data Create and build model Transform: shape Transform: clean 3
4 1 2 4
5 SQL: IBM SystemR 1970s Optimizer SQL Query: SELECT FROM WHERE Execution Plan Employee Skills Hardware Data
6 Declarative Machine Learning: IBM SystemML 2000s Optimizer ML Call: SVM.train() Execution Plan Employee Skills Hardware Data
7 And then there was a movement in open source Apache Spark, finally, a chance to push declarative data science mainstream!
8 Apache Spark One of the most important open source projects in a decade Significantly faster than MapReduce APIs for Python, R, Java, Scala Combine SQL, streaming, complex analytics Runs everywhere World's largest investment in Apache Spark Opened Spark Technology Center 3500 IBM Researchers and Developers to Spark
9 Apache Spark Blends Multiple Data Types, Sources, & Workloads Execute SQL Statements Streaming Analytics via Micro-batch M.L. and Statistical Algorithms Distributed Graph Processing Framework Spark SQL Spark Streaming MLlib Machine Learning Graph General compute engine Basic I/O functions Task dispatching Scheduling Spark Core Data Sources IBM Cloud Public Cloud Cloud Apps On-Premises BigInsights (HDFS) Cloudant (DBaaS) dashdb (Analytics) 9 SQDB (Manage d DB2) Swift (Object Storage)
10 Motivation for Apache Spark Traditional Approach: MapReduce jobs for complex jobs, interactive query, and online event-hub processing involves lots of (slow) disk I/O Input HDFS Read CPU Iteration 1 Memory HDFS Write HDFS Read CPU Iteration 2 Memory HDFS Write Result 10
11 Motivation for Apache Spark Traditional Approach: MapReduce jobs for complex jobs, interactive query, and online event-hub processing involves lots of (slow) disk I/O Input HDFS Read CPU Iteration 1 Memory HDFS Write HDFS Read CPU Iteration 2 Memory HDFS Write Result Solution: Keep more data in-memory with a new distributed execution engine Input HDFS Read CPU Iteration 1 Memory Zero Read/Write Disk Bottleneck CPU Iteration 2 Memory Chain Job Output into New Job Input x faster than network & disk
12 Comparing MapReduce and Spark Spark Hadoop MapReduce Storage No built-in storage (in-memory) On-disk only Operations Map, Reduce, Join, Sample, etc. Map and Reduce Execution Model Batch, interactive, streaming Batch only Programming Environments Python, Scala, Java, and R Java only Spark is a unified platform for data integration Contrast with the many distinct distributions & tools for Hadoop Spark follows lazy evaluation of execution graphs Optimizes jobs, reduces wait states, and allows easier pipelining of tasks Spark lowers the resource overhead for starting or shuffling jobs Less expensive than MapReduce 12
13 Infusing Analytics Applications with Apache Spark Spark is a unified framework for building analytics applications Performant Architecture Productive Workflows Leverages Existing Investments Continually Improving Project RedRock
14 IBM donates SystemML to open source to strengthen Spark machine learning IBM Research Apache SystemML systemml.apache.org
15 Cost Models for Heterogeneous Architectures Optimizer Improving Scala Automating Model Selection via Hyperparameter Heuristics 15
16 Example of how GPU Acceleration can be added to SystemML Optimizer Language Algebraic Rewriter Piggybacking Compiler Memory Estimator Cost Model Instructions Runtime Operator Selector Recompiler In-memory Instructions GPU Instructions Spark Job Instructions Java Application (SystemML) GPU Memory Manager In-Memory Single Node Cluster CUDA4J Java Native Interface GPU Memory Manager cublas cusparse Custom GPU Kernels 16
17 All of this innovation in Big Data Platforms will continue to flow into OPTIMIZERs so that Data Scientists can focus on being Data Scientists Deliver and deploy model Understand problem and domain Communicate results Ingest data Evaluate Explore and understand data Create and build model Transform: shape Transform: clean 17
18 So, where can I Learn More? 1 Spark Technology Center spark.tc 2 Apache SystemML systemml.apache.org 3 IBM Data Science Experience datascience.ibm.com
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