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Introduction to AWS Big Data
- 1. Introduction to AWSBig Data Omid Vahdaty, Big Data Ninja
- 2. When the dataoutgrows your ability to process ● Volume ● Velocity ● Variety
- 3. EMR ● Basic, thesimplest distribution of all hadoop distributions. ● Not as good as cloudera.
- 4. Collect ● Firehose ● Snowball ●SQS ● Ec2
- 5. Store ● S3 ● Kinesis ●RDS ● DynmoDB ● Clloud Search ● Iot
- 6. Process + Analyze ●Lambda ● EMR ● Redshift ● Machine learning ● Elastic search ● Data Pipeline ● Athena
- 7. Visulize ● Quick sight ●ElasticSearch service
- 8. History of tools HDFS- from google Cassandra - from facebook, Columnar store NoSQL , materialized view, secondary index Kafka - linkedin. Hbase, no sql , hadoop eco system.
- 9. EMR Hadoop ecosystem ●Spark - recommend options. Can do all the below… ● Hive ● Oozie ● Mahout - Machine library (mllib is better, runs on spark) ● Presto, better than hive? More generic than hive. ● Pig - scripting for big data. ● Impala - Cloudera, and part of EMR.
- 10. ETL Tools ● Attunity ●Splunk ● Semarchy ● Informatica ● Tibco ● Clarit
- 11. Architecture ● Decouple : ○Store ○ Process ○ Store ○ Process ○ insight... ● Rule of thumb: 3 tech in dc, 7 techs max in cloud ○ Do use more b/c: maintenance
- 12. Architecture considerations ● unStructured?Structured? Semi structured? ● Latency? ● Throughput? ● Concurrency? ● Access patterns? ● Pass? Max 7 technologies ● Iaas? Max 4 technologies
- 13. EcoSystem ● Redshift =analytics ● Aurora = OLTP ● DynamoDB = NoSQL like mongodb. ● Lambda ● SQS ● Cloud Search ● Elastic Search ● Data Pipeline ● Beanstalk - i have jar, install it for me… ● AWS machine learning
- 14. Data ingestions Architecturechallenges ● Durability ● HA ● Ingestions types: ○ Batch ○ Stream ● Transaction: OLTP, noSQL.
- 15. Ingestion options ● Kinesis ●Flume ● Kafka ● S3DistCP copy from ○ S3 to HDFS ○ S3 to s3 ○ Cross account ○ Supports compression.
- 16. Transfer ● VPN ● DirectConnect ● S3 multipart upload ● Snowball ● IoT
- 17. Steaming ● Streaming ● Batch ●Collect ○ Kinesys ○ DynamoDB streams ○ SQS (pull) ○ SNS (push) ○ KAFKA (Most recommend)
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- 19. Streaming ● Low latency ●Message delivery ● Lambda architecture implementation ● State management ● Time or count based windowing support ● Fault tolerant
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- 21. Stream collection options Kinesisclient library AWS lambda EMR Third party Spark streaming (latency min =1 sec) , near real time, with lot of libraries. Storm - Most real time (sub millisec), java code based. Flink (similar to spark)
- 22. Kinesys ● Stream -collect@source and near real time processing ○ Near real time ○ High throughput ○ Low cost ○ Easy administration - set desired level of capacity ○ Delivery to : s3,redshift, Dynamo ○ Ingress 1mb, egress 2mbs. Upto 1000 Transaction per second. ● Analytics - in flight analytics. ● Firehose - Park you data @ destination.
- 24. Kinesis analytics example CREATESTREAM "INTERMEDIATE_STREAM" ( hostname VARCHAR(1024), logname VARCHAR(1024), username VARCHAR(1024), requesttime VARCHAR(1024), request VARCHAR(1024), status VARCHAR(32), responsesize VARCHAR(32) ); -- Data Pump: Take incoming data from SOURCE_SQL_STREAM_001 and insert into INTERMEDIATE_STREAM
- 25. KCL ● Read fromstream using get api ● Build application with the KCL ● Leverage kinesis spout for storm ● Leverage EMR connector
- 26. Firehose - forparking ● Not for fast lane - no in flight analytics ● Capture , transform and load. ○ Kinesis ○ S3 ○ Redshift ○ elastic search ● Managed Service ● Producer - you input to delivery stream ● Buffer size MB ● Buffer in seconds.
- 27. Comparison of Kinesisproduct ● Stream ○ Sub 1 sec processing latency ○ Choice of stream processor (generic) ○ For smaller events ● Firehose ○ Zero admin ○ 4 targets built in (redshift, s3, search, etc) ○ Latency 60 sec minimum. ○ For larger “events”
- 28. DynamoDB ● Fully managedNoSql document store key value ○ Tables have not fixed schema ● High performance ○ Single digit latency ○ Runs on solid drives ● Durable ○ Multi az ○ Fault toulerant, replicated 3 AZ.
- 29. Durability ● Read ○ Eventuallyconsistent ○ Strongly consistent ● Write ○ Quorum ack ○ 3 replication - always - we can’t change it. ○ Persistence to disk.
- 30. Indexing and partitioning ●Idnexing ○ LSI - local secondary index, kind of alternate range key ○ GSI - global secondary index - “pivot charts” for your table, kind of projection (vertica) ● Partitioning ○ Automatic ○ Hash key speared data across partitions
- 31. DynamoDB Items andAttributes ● Partitions key ● Sort key (optional) ● LSI ● attributes
- 32. AWS Titan(on DynamoDB)- Graph DB ● Vertex - nodes ● Edge- relationship b/w nodes. ● Good when you need to investigate more than 2 layer relationship (join of 4 tables) ● Based on TinkerPop (open source) ● Full text search with lucene SOLR or elasticsearch ● HA using multi master replication (cassandra backed) ● Scale using DynamoDB backed. ● Use cases: Cyber , Social networks, Risk management
- 33. Elasticache: MemCache /Redis ● Cache sub millisecond. ● In memory ● No disk I/O when querying ● High throughput ● High availability ● Fully managed
- 34. Redshift ● Peta scaledatabase for analytics ○ Columnar ○ MPP ● Complex SQL
- 35. RDS ● Flavours ○ Mysql ○Auraora ○ postgreSQL ○ Oracle ○ MSSQL ● Multi AZ, HA ● Managed services
- 36. Data Processing ● Batch ●Ad hoc queries ● Message ● Stream ● Machine learning ● (by the use parquet, not ORC, more commonly used in the ecosystem)
- 37. Athena ● Presto ● Inmemory ● Hive meta store for DDL functionality ○ Complex data types ○ Multiple formats ○ Partitions
- 38. EMR ● Emr ,pig and hive can work on top of spark , but not yet in EMR ● TEZ is not working. Horton gave up on it. ● Can ran presto on hive
- 39. Best practice ● Bzip2- splittable compression ○ You don't have to open all the file, you can bzip2 only one block in it and work in parallel ● Snappy - encoding /decoding time - VERY fast, not compress well. ● Partitions ● Ephemeral EMR
- 40. EMR ecosystem ● Hive ●Pig ● Hue ● Spark ● Oozie ● Presto ● Ganglia ● Zookeeper ● zeppelin ● For research - Public data sets exist of AWS...
- 41. EMR ARchitecture ● Masternode ● Core nodes - like data nodes (with storage) ● Task nodes - (not like regular hadoop, extends compute) ● Default replication factor ○ Nodes 1-3 ⇒ 1 factor ○ Nodes 4-9 ⇒ 2 replication factor ○ Nodes 10+ ⇒ 3 replication factor ○ Not relevant in external tables ● Does Not have Standby Master node ● Best for transient cluster (goes up and down every night)
- 42. EMR ● Use SCALA ●If you use Spark SQL , even in python it is ok. ● For code - python is full of bugs, connects well to R ● Scala is better - but not connects easily to data science R ● Use cloud formation when ready to deploy fast. ● Check instance I , ● Dense instance is good architecture ● User spot instances - for the tasks. ● Use TAGs ● Constant upgrade of the AMI version ● Don't use TEZ ● Make sure your choose instance with network optimized ● Resize cluster is not recommended ● Bootstrap to automate cluster upon provisioning ● Steps to automate steps on running cluster ● Use RDS to share Hive MetaStore (the metastore is mysql based) ● Use r, kafka and impala via bootstrap actions and many others
- 43. CPU features forinstances
- 44. Sending work toemr ● Steps - ○ can be added while cluster is running ○ Can be added from the UI / CLI ○ FIFO scheduler by default ● Emr api ● Ganglia: jvm.JvmMetrics.MemHeapUsedM
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- 46. Hive ● SQL overhadoop. ● Engine: spark, tez, ML ● JDBC / ODBC ● Not good when need to shuffle. ● Connect well with DynamoDB ● SerDe json, parquet,regex etc.
- 47. Hbase ● Nosql likecassandra ● Below the Yarn ● Agent of HBASE on each data node. Like impala. ● Write on HDFS ● Multi level Index. ● (AWS avoid talking about it b/c of Dynamo, used only when you want to save money) ● Driver from hive (work from hive on top of HBase)
- 48. Presto ● Like Hive,from facebook. ● Not good always for join on 2 large tables. ● Limited by memory ● Not fault tolerant like hive. ● Optimized for ad hoc queries
- 49. Pig ● Distributed Shellscripting ● Generating SQL like operations. ● Engine: MR, Tez ● S3, DynamoDB access ● Use Case: for data science who don't know SQL, for system people, for those who want to avoid java/scala ● Fair fight compared to hive in term of performance only ● Good for unstructured files ETL : file to file , and use sqoop.
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- 51. Mahut ● Machine libraryfor hadoop - Not used ● Spark ML lib is used instead.
- 52. R ● Open sourcepackage for statistical computing. ● Works with EMR ● “Matlab” equivalent ● Works with spark ● Not for developer :) for statistician ● R is single threaded - use spark R to distribute. Not everything works perfect.
- 53. Apache Zeppelin ● Notebook- visualizer ● Built in spark integration ● Interactive data analytics ● Easy collaboration. ● Uses SQL ● work s on top of Hive ● Inside EMR. ● Give more feedback to let you know where u are
- 54. Hue● Hadoop userexperience ● Logs in real time and failures. ● Multiple users ● Native access to S3. ● File browser to HDFS. ● Manipulate metascore ● Job Browser ● Query editor ● Hbase browser ● Sqoop editor, oozier editor, Pig Editor
- 55. Spark ● In memory ●X10 to X100 times faster ● Good optimizer for distribution ● Rich API ● Spark SQL ● Spark Streaming ● Spark ML (ML lib) ● Spark GraphX (DB graphs) ● SparkR
- 56. Spark ● RDD ○ Anarray (data set) ○ Read only distributed objects cached in mem across cluster ○ Allow apps to keep working set in mem for reuse ○ Fault tolerant ○ Ops: ■ Transformation: map/ filter / groupby / join ■ Actions: count / reduce/ collet / save / persist ○ Object oreinted ops ○ High level expression liken lambda, funcations, map
- 57. Data Frames ● Datasetcontaining named columns ● Access ordinal positions ● Tungsten execution backed - framework for distributed memory (open source?) ● Abstraction for selecting , filtering, agg, and plot structure data. ● Run sql on it.
- 58. Streaming ● Near realtime (1 sec latency) , like batch of 1sec windows ● Same spark as before ● Streaming jobs with API
- 59. Spark ML ● Classification ●Regression ● Collaborative filtering ● Clustering ● Decomposition ● Code: java, scala, python, sparkR
- 60. Spark GraphX ● Workswith graphs for parallel computations ● Access to access of library of algorithm ○ Page rank ○ Connected components ○ Label propagation ○ SVD++ ○ Strongly connected components
- 61. Hive on Spark ●Will replace tez.
- 62. Spark flavours ● Owncluster ● With yarn ● With mesos
- 63. Downside ● Compute intensive ●Performance gain over mapreduce is not guaranteed. ● Streaming processing is actually batch with very small window.
- 64. Redshift ● OLAP, notOLTP→ analytics , not transaction ● Fully SQL ● Fully ACID ● No indexing ● Fully managed ● Petabyte Scale ● MPP ● Can create slow queue for queries which are long lasting. ● DO NOT USE FOR transformation.
- 65. EMR vs Redshift ●How much data loaded and unloaded? ● Which operations need to performed? ● Recycling data? → EMR ● History to be analyzed again and again ? → emr ● What the data needs to end up? BI? ● Use spectrum in some use cases. ● Raw data? s3.
- 66. Hive VS. Redshift ●Amount of concurrency ? low → hive, high → redshift ● Access to customers? Redshift? ● Transformation, Unstructured , batch, ETL → hive. ● Peta scal ? redshift ● Complex joins → Redshift
- 67. Presto VS redshift ●Not a true DW , but can be used. ● Require S3, or HDFS ● Netflix uses presoto for analytics.
- 68. Redshift ● Leader node ○Meta data ○ Execution plan ○ Sql end point ● Data node ● Distribution key ● Sort key ● Normalize… ● Dont be afraid to duplicate data with different sort key
- 69. Redshift Spectrum ● Externaltable to S3 ● Additional compute resources to redshift cluster. ● Not good for all use cases
- 70. Cost consideration ● Region ●Data out ● Spot instances ○ 50% - 80% cost reduction. ○ Limit your bid ○ Work well with EMR. use spot instances for task core mostly. For dev - use spot. ○ May be killed in the middle :) ● reserved instances
- 71. Kinesis pricing ● Streams ○Shard hour ○ Put payload unit , 25KB ● Firehose ○ Volume ○ 5KB ● Analytics ○ Pay per container , 1 cpu, 4 GB = 11 Cents
- 72. Dynamo ● Most expensive:Pay for throughput ○ 1k write ○ 4k read ○ Eventually consistent is cheaper ● Be carefull… from 400$ to 10000$ simply b/s you change block size. ● You pay for storage, until 25GB free, the rest is 25 cent per GB
- 73. Optimize cost ● Alertsfor cost mistakes- ○ unused machines etc. ○ on 95% from expected cost. → something is wrong… ● No need to buy RI for 3 year, 1year is better.
- 74. Visualizing - QuickSight ● Cheap ● Connect to everything ○ S3 ○ Dynamo ○ Redhsift ● SPICE ● Use storyboard to create slides of graphs like PPT
- 75. Tableau ● Connect toRedshift ● Good look and feel ● ~1000$ per user ● Not the best in term of performance. Quick sight is faster.
- 76. Other visualizer ● Tibco- Spot file ○ Has many connector ○ Had recommendations feature ● Jaspersoft ○ Limited ● ZoomData ● Hunk - ○ Users EMR and S3 ○ Scehmoe on the fly ○ Available in market place ○ Expensive. ○ Non SQL , has it own language.
- 77. Visualize - whichDB to choose? ● Hive is not recommended ● Presto a bit faster. ● Athena is ok ● Impala is better (has agent per machine, caching) ● Redshift is best.
- 78. Orchestration ● Oozie ○ Openssource workflow ■ Workflow: graph of action ■ Coordinator: scheduler jobs ○ Support: hive, sqoop , spark etc. ● Data pipeline ○ Move data from on prem to cloud ○ Distributed ○ Integrate well with s3, dynamodb, RDS, EMR, EC2, redshift ○ Like ETL: Input, data manipulation, output ○ Not trivial, but nicer than Oozie ● Other: AirFlow, Knime, Luigi, Azkaban
- 79. Security ● Shared securitymodel ○ Customer: ■ OS, platform, identity, access management,role, permissions ■ Network ■ Firewall ○ AWS: ■ compute, storage, databases, networking, LB ■ Regions, AZ, edge location ■ compliance
- 80. EMR secured ● IAM ●VPC ● Private subnet ● VPC endpoint to S3 ● MFA to login ● STS + SAML - token to login. Complex solution ● Kerberos authentication of Hadoop nodes ● Encryption ○ SSH to master ○ TLS between nodes
- 81. IAM ● User ● Bestpractice : ○ MFA ○ Don't use ROOT account ● Group ● Role ● Policy best practice ○ Allow X ○ Disallow all the rest ● Identity federation via LDAP
- 82. Kinesis Security bestpractice ● Create IAM admin for admin ● Create IAM entity for re sharing stream ● Create IAM entity for produces to write ● Create iam entity for consumer to read. ● Allow specific source IP ● Enforce AWS:SecureTransport condition key for every API call. ● Use temp credentials : IAM role
- 83. Dynamo Security ● Access ●Policies ● Roels ● Database level access level - row level (item)/ Col level (attribute) access control ● STS for web identity federation. ● Limit of amount row u can see ● Use SSL ● All request can be signed bia SHA256 ● PCI, SOC3, HIPPA, 270001 etc. ● Cloud trail - for Audit log
- 84. Redshift Security ● SSLin transit ● Encryption ○ KMSAES256 ○ HSM encryption (hardware) ● VPC ● Cloud Trail ● All the usual regulation certification. ● Security groups
- 85. Big Data patterns ●Interactive query → mostly EMR, Athena ● Batch Processing → reporting → redshift + EMR ● Stream processing → kinesis, kinesis client library, lambda, EMR ● Real time prediction → mobile, dynamoDB. → lambda+ ML. ● Batch prediction → ML and redshift ● Long running cluster → s3 → EMR ● Log aggregation → s3 → EMR → redshift
- 86. Stay in touch... ●Omid Vahdaty ● +972-54-2384178