Learning Cloudera Impala – Book Availability

Learning Cloudera Impala:

 

Using Cloudera Impala is for those who really want to take advantage of their Hadoop cluster by processing extremely large amounts of raw data in Hadoop at real-time speed. Prior knowledge of Hadoop and some exposure to HIVE and MapReduce is expected.

 

Learning Cloudera Impala

You will learn from this book:

• Understand the various ways of installing Impala in your Hadoop cluster
• Use the Impala shell API to interact with Impala components
• Utilize Impala Query Language and built-in functions to play with data
• Administrate and fine-tune Impala for high availability
• Identify and troubleshoot problems in a variety of ways
• Get acquainted with various input data formats in Hadoop and how to use them with Impala
• Comprehend how third party applications can connect with Impala to provide data visualization and various other enhancements

Available at:

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Spark Summit 2013- Mon Dec 2, 2013 Keynotes

The State of Spark, and Where We’re Going Next

Matei Zaharia (CTO, Databricks; Assistant Professor, MIT)

Community Contributions for Spark

• YARN support (Yahoo!)
• Columnar compression in Shark (Yahoo!)
• Fair scheduling (Intel)
• Metrics reporting (Intel, Quantifind)
• New RDD operators (Bizo, ClearStory)
• Scala 2.10 support (Imaginea)

Downloads: pptx slides, pdf slides

Turning Data into Value
Ion Stoica (CEO, Databricks; CTO, Conviva; Co-Director, UC Berkeley AMPLab)

• Everyone collects but few extract value from data
• Unification of comp. and prog. models key to
  • » Efficiently analyze data
  • » Make sophisticated, real-time decisions
• Spark is unique in unifying
  • » batch, interactive, streaming computation models
  • » data-parallel and graph-parallel prog. models

Downloads: pptx slides, pdf slides

Big Data Research in the AMPLab
Mike Franklin (Director, UC Berkeley AMPLab)

• GraphX: Unifying Graph Parallel & Data Parallel Analytics
• OLTP and Serving Workloads •  MDCC: Mutli Data Center Consistency
• HAT: Highly-Available Transactions
• PBS: Probabilistically Bounded Staleness
• PLANET: Predictive Latency-Aware Networked Transactions
• Fast Matrix Manipulation Libraries
• Cold Storage, Partitioning, Distributed Caching
• Machine Learning Pipelines, GPUs,

Downloads: pptx slides, pdf slides

Hadoop and Spark Join Forces in Yahoo
Andy Feng (Distinguished Architect, Cloud Services, Yahoo)

YAHOO AT SCALE:

• 150 PB of data on Yahoo Hadoop clusters
  • Yahoo data scientists need the data for
    • Model building
    • BI analytics
  • Such datasets should be accessed efficiently
    • avoid latency caused by data movement
• 35,000 servers in Hadoop cluster
  • Science projects need to leverage all these servers for computation

SOLUTION: HADOOP + SPARK

• science … Spark API & MLlib ease development of ML algorithms
• speed … Spark reduces latency of model training via in-memory RDD etc
• scale … YARN brings Hadoop datasets & servers at scientists’ fingertips

Downloads: pdf slides (large file)

Integration of Spark/Shark into the Yahoo! Data and Analytics Platform

Tim Tully (Distinguished Engineer/Architect, Yahoo)

• Legacy / Current Hadoop Architecture
• Reflection / Pain Points
• Why the movement towards Spark / Shark
• New Hybrid Environment
• Future Spark/Shark/Hadoop Stack

 

Downloads:  pptx slides, pdf slides

 

Spark in the Hadoop Ecosystem
Eric Baldeschwieler (@jeric14)

Data scientists & Developers need an open standard for sharing their Algorithms & functions, an “R” for big data. 

• Spark best current candidate:
•  Open Source – Apache Foundation
•  Expressive (MR, iteration, Graphs, SQL, streaming)
•  Easily extended & embedded (DSLs, Java, Python…)

Spark “on the radar”
•  2008 – Yahoo! Hadoop team collaboration w Berkeley Amp/Rad lab begins
•  2009 – Spark example built for Nexus -> Mesos
•  2011 – “Spark is 2 years ahead of anything at Google””- Conviva seeing good results w Spark
•  2012 – Yahoo! working with Spark / Shark
•  Today – Many success stories” – Early commercial support

Downloads: ppt slides, pdf slides

Keywords: Apache Spark, Hadoop, YARN,  Big Data, Mesos, Databricks, Conviva,