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1
APACHE SPARK
AND QUBOLE
ACCELERATING TIME TO
VALUE OF BIG DATA
January 2019
2
What Is Apache Spark?
Apache Spark is a high-performance, distributed data processing engine that data
practitioners prefer for handling big data workloads. First developed at the AMPLab at UC
Berkeley, Spark has become a widely adopted framework for machine learning, complex
data processing, advanced analytics, and other big data projects.
Machine
Learning
Stream
Processing
Data Preparation
and Processing
Batch
Processing
Graph
Processing
Scala · Java
Python · R · SQL
3
A Vibrant and Engaged Community Fuels Spark’s Growth
Spark’s vibrant community of contributors prioritizes agility, flexibility, and scalability
through hundreds of code deployments per month. Spark is the leading big data
framework in use, with a usage increase of 29% over 2017.
* Source: Big Data Survey, 2018
*
4
Data Growth Drives the Need for Spark
As networked devices, instruments, and applications proliferate globally, enterprises
are capturing and processing ever-increasing mountains of data. Businesses must
access and integrate information from numerous sources to develop the insights and
analytics required to drive business strategies and make effective decisions.
Data Creation Exploding at Exponential Rates
Source: IDC’s Data Age 2025 Study, sponsored by Seagate, April 2017
10x
INCREASE
in data created
5
Intensive Processing Is Required to Create Usable Datasets
Traditional tools are not suited to meet user requirements of data today. Saddled
with the additional cost of inefficient data processing, organizations have far less time
and resources for experimentation. The additional cost of experimentation cripples
innovation and reduces the return on investment on big data investments.
6
Distributed Processing to the Rescue
The inability of traditional tools to keep pace with the explosion of data has led to the
emergence of distributed processing engines — such as Hadoop and Spark — that split
the data into smaller, manageable chunks and process it across multiple computing
nodes. Distributed engines greatly improve processing times and enable a wide
spectrum of use cases in machine learning and big data analytics, which in turn lead to
more experimentation and greater innovation.
7
The Evolution of Distributed Engines
Both Hadoop and Spark are distributed processing engines, but they’re not
interchangeable. While Spark improves the speed of data processing significantly,
Hadoop can still process larger volumes of data. You should probably pick the one
that most closely addresses your specific use case, workload type, and data volume.
Comparison of Data Processing Engines
8
Apache Spark on Qubole
Customers trust Qubole to process and manage their Spark workloads. Qubole’s
enhancements improve cost efficiency, performance, and usability while ensuring
the security and reliability required by enterprise applications.
Enterprise-
Ready
Reliability
Security
Usability
Performance Lower
Costs
Qubole boosts the power of Spark to exceed
the demands of enterprise workloads
9
Qubole Reduces Computing Costs of Apache Spark
Cloud infrastructure costs can quickly spiral out of control. Qubole’s advanced cost
controls have enabled our customers to see as much as a 50% reduction in their
cloud computing costs.
Intelligent AWS Spot Management
Automatically bids on Spot Instances and rebalances Spot Nodes. Utilizes
low-cost compute resources without causing cascading failures.
Workload-Aware Autoscaling
Advanced SLA-based scaling algorithm determines the exact number of
executors to optimize resource utilization.
Aggressive Downscaling
Decommission idle and unneeded compute nodes. Use container packing
to downscale with higher resource utilization.
Heterogeneous Clusters
Mix different instance types in the same cluster to create significant savings
and more reliable clusters.
10
Qubole Improves Apache Spark Performance
for Big Data Workloads
Big data processing efficiency depends on performance, especially read/write and
data operations speed. As a standalone, open-source solution, Spark is not inherently
optimized for enterprise big data workloads.
Qubole adds performance optimizations and smart management tools that extend the
power of Spark to even the most complex big data problems.
Direct Writes
Faster write throughput when writing to Amazon S3, alleviating the need to
stage writes before committing them.
Fast Caching with RubiX
Platform-wide caching layer reduces I/O latency and speeds up data engines.
Join Optimizations
Significantly improve Spark performance of join operations on large datasets.
Performance Optimization
Added visibility and optimal configuration recommendations for Spark applications.
11
Qubole Improves the Usability of Apache Spark
The open-source version of Apache Spark is powerful, but extremely complex for anyone
who’s not an expert Spark developer.
By handling back-end configuration issues and automating day-to-day processes, Qubole
makes the Spark world approachable for data engineers, data analysts, data scientists,
and administrators.
Multiple Interfaces
Launch Spark jobs via Analyze, Notebooks, or API interfaces as required.
Spark Clusters
Automate provisioning, scaling, and termination to optimize compute resources.
Workflow Automation
Schedule Spark jobs or leverage Airflow to build end-to-end pipelines.
Spark UI
Simplify and speed debugging of problematic jobs.
Package Management
Auto-distribute Python and R packages using predefined dependencies.
12
Apache Spark on Qubole: Data to Intelligence to Action
Apache Spark on Qubole supports the ingestion, preparation, integration, transformation,
and analysis of data coming in from sources across your extended enterprise — and
converts it into actionable intelligence that arms users with the insights they need to make
decisions for maximum impact.
13
Users Trust Qubole for Their Spark Workloads
Qubole’s industry-leading, distributed-computing platform delivers efficiencies
for departmental applications to the largest of Spark clusters in distributed cloud-
computing environments.
Qubole Provides
Industrial-Strength
Scale and Reliability
Support for even
the largest clusters
750+
C O N C U R R E N T
N O D E C L U S T E R S *
Unbridled Growth
in Spark Usage
on Qubole
2018 annual increase
in Spark commands
+439%
I N C R E A S E
O V E R 2 0 1 8 *
* Source: 2018 Qubole Big Data Activation Report
14
Case Study: Return Path
Return Path uses Qubole to deliver self-service analytics, simplify infrastructure, reduce
costs, improve team productivity, and accelerate time-to-value on data science projects.
Reduced cloud
compute costs
Higher
productivity
Increased
innovation
Greater customer
satisfaction
“Qubole helped prevent us from making bad decisions that would
have cost the business tens or hundreds of thousands of dollars.”
Robert Barclay, VP of Data and Analytics, Return Path
15
Test Drive Apache Spark on Qubole Today
To learn more, visit qubole.com
The #1 Cloud-Native Data Platform
for Machine Learning and Analytics
Take Qubole for a test drive today. See how data-driven
industry leaders work smarter and slash cloud costs with Qubole.
Build data pipelines
with ease
Bring machine learning
to production
Analyze any type of
data from any source
Copyright © 2019 Qubole, Inc. All rights reserved.
Start Your Qubole
Test Drive Now
Start Your Qubole
Test Drive Now

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Ebooks - Accelerating Time to Value of Big Data of Apache Spark | Qubole

  • 1. 1 APACHE SPARK AND QUBOLE ACCELERATING TIME TO VALUE OF BIG DATA January 2019
  • 2. 2 What Is Apache Spark? Apache Spark is a high-performance, distributed data processing engine that data practitioners prefer for handling big data workloads. First developed at the AMPLab at UC Berkeley, Spark has become a widely adopted framework for machine learning, complex data processing, advanced analytics, and other big data projects. Machine Learning Stream Processing Data Preparation and Processing Batch Processing Graph Processing Scala · Java Python · R · SQL
  • 3. 3 A Vibrant and Engaged Community Fuels Spark’s Growth Spark’s vibrant community of contributors prioritizes agility, flexibility, and scalability through hundreds of code deployments per month. Spark is the leading big data framework in use, with a usage increase of 29% over 2017. * Source: Big Data Survey, 2018 *
  • 4. 4 Data Growth Drives the Need for Spark As networked devices, instruments, and applications proliferate globally, enterprises are capturing and processing ever-increasing mountains of data. Businesses must access and integrate information from numerous sources to develop the insights and analytics required to drive business strategies and make effective decisions. Data Creation Exploding at Exponential Rates Source: IDC’s Data Age 2025 Study, sponsored by Seagate, April 2017 10x INCREASE in data created
  • 5. 5 Intensive Processing Is Required to Create Usable Datasets Traditional tools are not suited to meet user requirements of data today. Saddled with the additional cost of inefficient data processing, organizations have far less time and resources for experimentation. The additional cost of experimentation cripples innovation and reduces the return on investment on big data investments.
  • 6. 6 Distributed Processing to the Rescue The inability of traditional tools to keep pace with the explosion of data has led to the emergence of distributed processing engines — such as Hadoop and Spark — that split the data into smaller, manageable chunks and process it across multiple computing nodes. Distributed engines greatly improve processing times and enable a wide spectrum of use cases in machine learning and big data analytics, which in turn lead to more experimentation and greater innovation.
  • 7. 7 The Evolution of Distributed Engines Both Hadoop and Spark are distributed processing engines, but they’re not interchangeable. While Spark improves the speed of data processing significantly, Hadoop can still process larger volumes of data. You should probably pick the one that most closely addresses your specific use case, workload type, and data volume. Comparison of Data Processing Engines
  • 8. 8 Apache Spark on Qubole Customers trust Qubole to process and manage their Spark workloads. Qubole’s enhancements improve cost efficiency, performance, and usability while ensuring the security and reliability required by enterprise applications. Enterprise- Ready Reliability Security Usability Performance Lower Costs Qubole boosts the power of Spark to exceed the demands of enterprise workloads
  • 9. 9 Qubole Reduces Computing Costs of Apache Spark Cloud infrastructure costs can quickly spiral out of control. Qubole’s advanced cost controls have enabled our customers to see as much as a 50% reduction in their cloud computing costs. Intelligent AWS Spot Management Automatically bids on Spot Instances and rebalances Spot Nodes. Utilizes low-cost compute resources without causing cascading failures. Workload-Aware Autoscaling Advanced SLA-based scaling algorithm determines the exact number of executors to optimize resource utilization. Aggressive Downscaling Decommission idle and unneeded compute nodes. Use container packing to downscale with higher resource utilization. Heterogeneous Clusters Mix different instance types in the same cluster to create significant savings and more reliable clusters.
  • 10. 10 Qubole Improves Apache Spark Performance for Big Data Workloads Big data processing efficiency depends on performance, especially read/write and data operations speed. As a standalone, open-source solution, Spark is not inherently optimized for enterprise big data workloads. Qubole adds performance optimizations and smart management tools that extend the power of Spark to even the most complex big data problems. Direct Writes Faster write throughput when writing to Amazon S3, alleviating the need to stage writes before committing them. Fast Caching with RubiX Platform-wide caching layer reduces I/O latency and speeds up data engines. Join Optimizations Significantly improve Spark performance of join operations on large datasets. Performance Optimization Added visibility and optimal configuration recommendations for Spark applications.
  • 11. 11 Qubole Improves the Usability of Apache Spark The open-source version of Apache Spark is powerful, but extremely complex for anyone who’s not an expert Spark developer. By handling back-end configuration issues and automating day-to-day processes, Qubole makes the Spark world approachable for data engineers, data analysts, data scientists, and administrators. Multiple Interfaces Launch Spark jobs via Analyze, Notebooks, or API interfaces as required. Spark Clusters Automate provisioning, scaling, and termination to optimize compute resources. Workflow Automation Schedule Spark jobs or leverage Airflow to build end-to-end pipelines. Spark UI Simplify and speed debugging of problematic jobs. Package Management Auto-distribute Python and R packages using predefined dependencies.
  • 12. 12 Apache Spark on Qubole: Data to Intelligence to Action Apache Spark on Qubole supports the ingestion, preparation, integration, transformation, and analysis of data coming in from sources across your extended enterprise — and converts it into actionable intelligence that arms users with the insights they need to make decisions for maximum impact.
  • 13. 13 Users Trust Qubole for Their Spark Workloads Qubole’s industry-leading, distributed-computing platform delivers efficiencies for departmental applications to the largest of Spark clusters in distributed cloud- computing environments. Qubole Provides Industrial-Strength Scale and Reliability Support for even the largest clusters 750+ C O N C U R R E N T N O D E C L U S T E R S * Unbridled Growth in Spark Usage on Qubole 2018 annual increase in Spark commands +439% I N C R E A S E O V E R 2 0 1 8 * * Source: 2018 Qubole Big Data Activation Report
  • 14. 14 Case Study: Return Path Return Path uses Qubole to deliver self-service analytics, simplify infrastructure, reduce costs, improve team productivity, and accelerate time-to-value on data science projects. Reduced cloud compute costs Higher productivity Increased innovation Greater customer satisfaction “Qubole helped prevent us from making bad decisions that would have cost the business tens or hundreds of thousands of dollars.” Robert Barclay, VP of Data and Analytics, Return Path
  • 15. 15 Test Drive Apache Spark on Qubole Today To learn more, visit qubole.com The #1 Cloud-Native Data Platform for Machine Learning and Analytics Take Qubole for a test drive today. See how data-driven industry leaders work smarter and slash cloud costs with Qubole. Build data pipelines with ease Bring machine learning to production Analyze any type of data from any source Copyright © 2019 Qubole, Inc. All rights reserved. Start Your Qubole Test Drive Now Start Your Qubole Test Drive Now