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Data Pipelines with Spark & DataStax Enterprise
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- 1. Simon Ambridge Data Pipelines With Spark & DSE An Introduction To Building Agile, Flexible and Scalable Big Data and Data Science Pipelines Version 0.8
- 2. Certified Apache Cassandra and DataStax enthusiast who enjoys explaining that the traditional approaches to data management just don’t cut it anymore in the new always on, no single point of failure, high volume, high velocity, real time distributed data management world. Previously 25 years implementing Oracle relational data management solutions. Certified in Exadata, Oracle Cloud, Oracle Essbase, Oracle Linux and OBIEE firstname.lastname@example.org @stratman1958 Simon Ambridge Pre-Sales Solution Engineer, Datastax UK
- 3. Introduction To Big Data Pipelines
- 4. Big, Static Data Fast, Streaming Data Big Data Pipelining: Classification Big Data Pipelines can mean different things to different people Repeated analysis on a static but massive dataset • An element of research – e.g. genomics, clinical trial, demographic data • Typically repetitive, iterative, shared amongst data scientists for analysis Real-time analytics on streaming data • Industrialised or commercial processes – sensors, tick data, bioinformatics, transactional data, real-time personalisation • Happening in real-time, data cannot be dropped or lost
- 5. Static Datasets All You Can Eat? Really.
- 6. Static Data Analytics : Traditional Tools Repeated iterations, at each stage Run/debug cycle can be slow Sampling Modeling InterpretTuning Reporting Re-sample Typical traditional ‘static’ data analysis model Data Results
- 7. Static Data Analytics : Scale Up Challenges Sampling and analysis often run on a single machine • CPU and memory limitations Limited sampling of a large dataset because of data size limitations • Multiple iterations over large datasets is frequently not an ideal approach
- 8. Static Data Analytics : Traditional Scaling DATA (GB) DATA (MB) DATA (TB) Small datasets, small servers Large datasets, large servers
- 9. Static Data Analytics: Big Data Problems Data is getting Really Big! • Data volumes are getting larger! • The number of data sources is exploding! • More data is arriving faster! Scaling up is becoming impractical • Physical limits • Datalimits • The validity of the analysis becomes obsolete, faster
- 10. Static Data Analytics : Big Data Needs We need scalable infrastructure + distributed technologies • Data volumes can be scaled • Distribute the data across multiple low-cost machines • Faster processing • More complex processing • No single point of failure
- 11. Static Data Analytics : DSE Delivers Building a distributed data processing framework can be a complex task! It needs to be: • Scalable • Fast in-memory processing • Replicated for resiliency • Batch and real-time data feeds • Ad-hoc queries DataStax delivers an integrated analytics platform
- 12. Cassandra: THE Web, IoT & Cloud Database What is Apache Cassandra? • Very fast • Extremely resilient • Across multiple data centres • No single point of failure • Continuous Availability, Disaster Avoidance • Linear scale • Easy to operate Enterprise Cassandra platform from Datastax
- 13. DataStax Enterprise DataStax Enterprise: Editions DataStax Enterprise Standard • DSE Standard is DataStax’s entry level commercial database offering • Represents the minimum recommended to deploy Cassandra in a production environment DataStax Enterprise Max • DSE Max is DataStax’s advanced commercial database offering • Designed for production Cassandra environments that have mixed workload requirements
- 14. Spark: THE Analytics Engine What is Apache Spark? • Distributed in-memory analytic processing • Batch and streaming analytics • Fast - 10x-100x faster than Hadoop MapReduce • Rich Scala, Java and Python APIs Tightly integrated with DSE
- 15. Spark: Dayton Gray Sort Contest Dayton Gray benchmark - tests how fast a system can sort 100 TB of data (1 trillion records) • Previous world record held by Hadoop MapReduce cluster of 2100 nodes, in 72 minutes • 2014: Spark completed the benchmark in 23 minutes on just 206 EC2 nodes = 3X faster using 10X fewer machines • Spark sorted 1 PB (10 trillion records) on 190 machines in < 4 hours. Previous Hadoop MapReduce time of 16 hours on 3800 machines = 4X faster using 20X fewer machines
- 16. DataStax Enterprise: Analytics Integration Cassandra Cluster Spark Cluster ETL Spark Cluster • Tight integration • Data locality • Microsecond response times X • Apache Cassandra for Distributed Persistent Storage • Integrated Apache Spark for Distributed Real-Time Analytics • Analytics nodes close to data - no ETL required X • Loose integration • Data separate from processing • Millisecond response times “Latency when transferring data is unavoidable. The trick is to reduce the latency to as close to zero as possible…”
- 17. Static Data Analytics : Requirements Valid data pipeline analysis methods must be: Auditable • Reproducible • Documented Controlled • Version control Collaborative • Accessible
- 18. Notebooks: Features What are Notebooks? • Drive your data analysis from the browser • Highly interactive • Tight integration with Apache Spark • Handy tools for analysts: • Reproducible visual analysis • Code in Scala, CQL, SparkSQL, Python • Charting – pie, bar, line etc • Extensible with custom libraries
- 19. Example: Spark Notebook Cells Markdown Output Controls
- 20. Static Data Analytics : Approach Example architecture & requirements 1. Optimised source data format 2. Distributed in-memory analytics 3. Interactive and flexible data analysis tool 4. Persistent data store 5. Visualisation tools
- 21. Static Data Analytics : Example ADAM Notebook Persistent Storage OLTP Database Visualisation Genome research platform - ADST (Agile Data Science Toolkit)
- 22. Static Data Analytics : Pipeline Process Flow 3. Persistent data storage 2. Interactive, flexible and reproducible analysis 1. Source data 4. Visualise and analyse
- 23. Static Data Analytics : Pipeline Scalability • Add more (physical or virtual) nodes as required to add capacity • Container tools ease configuration management and deployment • Scale out quickly
- 24. Static Data Analytics : Now • No longer an iterative process constrained by hardware limitations • Now a more scalable, resilient, dynamic, interactive process, easily shareable Analyse The new model for large-scale static data analytics Share X Load SCALE & DISTRIBUTE PROCESSING
- 25. Real-Time Datasets If it’s Not “Now”, Then It’s Probably Already Too Late
- 26. Big Data Pipelining: Why Real-Time? • React to customers faster and with more accuracy • Reduce risk through more accurate understanding of the market • Optimise return on marketing investment • Faster time to market • Improve efficiency In a highly connected world In most cases ‘real-time’ data changing at <1s intervals
- 27. Big Data Pipelining: Real-Time Analytics • Capture, prepare, and process fast streaming data • Different approach from traditional batch processing • The speed of now – cannot wait • Immediate insight, instant decisions What problem are we trying to solve?
- 28. Big Data Pipelining: Real-Time Use Cases Sensor data (IoT) Transactional data User Experience Social media Use cases for streaming analytics
- 29. Big Data Analytics: Streams Data tidal waves!Netflix • Ingests Petabytes of data per day • Over 1 TRILLION transactions per day (>10 m per second) into DSE Data streams? Data torrent?
- 30. Big Data Pipelining: Real-Time architecture Analytics in real-time, at scale Fast processing, distributed, in-memory Increasingly using a technology stack comprising Kafka, Spark and Cassandra • Scalable • Distributed • Resilient Streaming analytics architecture - what do we need?
- 31. Kafka: Architecture How Does Kafka Work? Kafka “De-couples” producers and consumers in data pipelines ’Producers’ send messages to the Kafka cluster, which in turn serves them up to ’Consumers’ • Kafka maintains feeds of messages in categories called topics • A Kafka cluster is comprised of one or more servers called a broker Producer Producer Producer Consumer Consumer Consumer Kafka Cluster
- 32. Kafka: Streaming With Spark Kafka writes, Spark reads • Topics can have multiple partitions • Each topic partition stored as a log (an ordered set of messages) • Messages are simply byte arrays, so can store any object in any format • Each message in a partition is assigned a unique offset Spark consumes messages as a stream, in micro batches, saved as RDD’s 1 2 3 4 5 6 7 8 Partition 0 1 2 3 4 5 6 7 8 Partition 1 1 2 3 4 5 6 Partition 0 Temperature Topic Rainfall Topic Temperature Consumer Rainfall Consumer Temperature Consumer
- 33. DataStax Enterprise: Streaming Schematic Sensor Network Signal Aggregation Services Messaging Queue Sensor Data Queue Management Broker Broker Collection Service Data Storage OLTP PersistenceLayer Streaming Data Ingest
- 34. DataStax Enterprise: Streaming Analytics Real-time Analytics Persistent Storage OLTP Database !$£€! Personalisation Actionable insight Monitoring Web / Analytics / BI
- 35. DataStax Enterprise: Multi-DC Uses DC: EUROPEDC: USA Real-time active-active geo-replication across physical datacentres 4 3 25 1 4 3 25 1 8 1 2 3 4 5 6 7 1 2 3 OLTP: Cassandra 5 4 Analytics: Cassandra + Spark Replication Replication Workload separation via virtual datacentres
- 36. Real-Time Analytics: DSE Multi-DC Workload Management and Separation With DSE Analytics / BI Analytics Datacentre OLTP Datacentre 100% Uptime, Global Scale OLTP Real-Time Analytics Mixed Load OLTP and Analytics Platform Replication Replication JDBC ODBC Separation of OLTP from Analytics Social Media IoT Personalisation & Persistence Personalisation !$£€! Actionable insight Monitoring App, Web
- 37. DSE & Analytics : Summary Static, Massive Data Scalable Data Pipelines 1. Optimised data storage formats 2. Scalable, distributed technologies 3. Flexible and interactive analysis tools 4. Resilient, persistent Storage Real-Time Streaming Data Scalable Data Pipelines 1. Scalable, distributed technologies 2. De-coupled Producers and Consumers 3. Real-Time analytics 4. Resilient, persistent Storage Spark Mesos Akka Cassandra Kafka
- 38. Thank you!