Talk given during Symbol & Key meetup on 7 Mar 2016

1. Its Place Within a Big Data Stack
Junjun Olympia

2. Image from http://mattturck.com/2016/02/01/big-data-landscape/

3. A Quick Review of What Spark Is

4. Spark is a fast, large-scale data processing engine
● Runs both in-memory and on-disk
● 10x-100x faster than Hadoop MapReduce
● Can be written in Java, Scala, Python, R, & SQL
● Supports both batch and streaming workflows
● Has several modules
○ Spark Core
○ Spark Streaming
○ Spark MLLib
○ GraphX

5. It is the most active open-source project in big data
Next three images from http://go.databricks.com/2015-spark-survey

6. Gaining adoption across industries and companies

7. Used to power several different systems

8. (Big) Data systems perform common functions

9. Capture and extract data

10. Data can come from several sources
● Existing databases and data warehouses
● Flat files from legacy systems
● Web, mobile, and application logs
● Data feeds from social media
● IoT devices

11. Extract from database: Sqoop vs Spark JDBC API
$ sqoop import --connect jdbc:postgresql:dbserver --table schema.tablename
--fields-terminated-by 't' --lines-terminated-by 'n'
--optionally-enclosed-by '"
val jdbcDF = sqlContext.read.format("jdbc").options(
Map("url" -> "jdbc:postgresql:dbserver",
"dbtable" -> "schema.tablename")).load()

12. Read JSON files
// JSON file as a dataframe
val df = sqlContext.read.json("people.json")
CREATE TEMPORARY TABLE people
USING org.apache.spark.sql.json
OPTIONS (path 'people.json')

13. Ingest streaming data from Kafka
import org.apache.spark.streaming.kafka._
val directKafkaStream = KafkaUtils.createDirectStream[
[key class], [value class], [key decoder class], [value decoder class] ](
streamingContext, [map of Kafka parameters], [set of topics to consume])
var offsetRanges = Array[OffsetRange]()
directKafkaStream.transform { rdd =>
offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
rdd
}.map {...}.foreachRDD { rdd => ... }

15. Transform Data

16. Data in an analytic pipeline usually need transformation
● Check and/or correct for data quality issues
● Handle missing values
● Cast values into specific data types or formats
● Compute derived fields
● Split or merge records to achieve desired granularity
● Join with another dataset (i.e. reference lookups)
● Restructure as required by downstream applications or target databases

17. There’s plenty of tools that do this
● Before big data
○ Informatica PowerCenter
○ Pentaho Kettle
○ Talend
○ SSIS
○ OWB
● Early Hadoop
○ Apache Pig
○ Hive via HQL
○ Plain ol’ MapReduce
● Spark core, Streaming, DataFrames

19. Store data

20. Data can then be stored several different ways
● As self-describing files like Parquet, Avro, JSON, XML
● Hive metastore-managed tables
● Other low-latency SQL-on-Hadoop engines (i.e. Impala, Drill, Kudu)
● Key-value and wide-table databases for fast random access (i.e. HBase,
Cassandra)
● Search databases (i.e. ElasticSearch, Solr)
● Conventional data warehouses and databases

22. Query, analyze, visualize

23. There’s plenty of tools here, too
● Databases offering JDBC/ODBC connectivity
○ Hive, Impala, Drill
○ MPP data warehouses
○ Spark SQL via JDBC Thrift Server
● BI Tools via SQL
○ Qlikview
○ Tableau
○ Pentaho BI
● For richer analyses beyond Spark SQL
○ Spark shell
○ Better with notebooks (i.e. Zeppelin, Jupyter)

26. Spark is an essential part of the modern big data
stack.

27. A unified framework such as Spark offers benefits, too
● Fewer moving pieces
● Smaller stack to administer and manage
● Common languages
● Familiar patterns
● Encourages team members to become cross-functional

28. Some common questions about Spark

29. Is Spark a database?

30. Fine; is it a data warehouse then?

31. Is Spark a Hadoop replacement?

32. Are there other technologies similar to Spark?

33. Questions?