Workload Automation + Hadoop?

Jul 11, 20160 likes627 views

Marcin Kleczynski founded Malwarebytes in 2004 after getting infected by malware as a teenager and wanting to help others. He taught himself coding and created free anti-malware software, which later became a paid version launched with a business partner. Malwarebytes has since grown significantly with over 500 million downloads worldwide and offices across several countries. It aims to create a malware-free world for all.

Workload Automa-on +
Hadoop?
Oh Yeah! …a Match Made in Heaven

The Malwarebytes Story
In 2004, around the age of 15, Marcin was trying to
download a video game from the internet, like most kids
his age.
In doing so, he infected his computer with multiple pieces
of malware.
Feeling let down that his antivirus had failed him, and with
the fear of being grounded, he set out to find a solution.
After three days of searching, Marcin found a group of
individuals online who were helping others like him who
had also been infected.
They patiently helped him clean his computer, and to
Marcin these people were superheroes.
It was then he began to teach himself how to code, and
wrote the very first version of anti-malware’s remediation
code. He gave it out for free.
Later Marcin partnered with Bruce Harrison to launch a
paid for version of his software with a lifetime license, for
only $25.00.
In 2008 they cofounded Malwarebytes with a mission to
create a malware-free existence for the world.
Since then, Malwarebytes has been downloaded over 500
million times worldwide, and has removed over 5 billion
infections.

Today, Malwarebytes has grown to more than 425
employees in 15 countries, and has offices in California,
Florida, Ireland, and Estonia. Malwarebytes has also
become a world leader in remediation and malware
prevention.
Malwarebytes is a leader in both the Endpoint
Protection Platform (EPP) and Endpoint Detection and
Response (EDR) spaces.
Marcin Kleczynski
Chief Executive Officer
Malwarebytes

– Our Vision
“We believe that everyone has a
fundamental right to a malware-free
existence”

– Our Mission
“To create the best disinfection and
protection solutions to combat the world’s
most harmful threats.”

“Everyone has a fundamental right to a malware-free existence”
1st
Fastest growing security
company over 100m run rate
250k
Downloads per day
2.4B
Protection events in 2015
500M+
Installs to date

“Everyone has a fundamental right to a malware-free existence”
10,000+
Businesses protected
750,000,000
IT hours saved
605,000
Threats blocked per hour
28,000,000+
Endpoints protected

Data Science and Engineering @ Malwarebytes
Data Engineering Data Operations
Data Science &
Analytics
Application Support

10
Products
Customers
Threat Marketing
Compliance
Marketing
Customer
Success
POV Tools

11
Data
is
Coming
Data Science and Engineering

“I didn’t come here
to learn about
Malwarebytes, I
like data…”

16
Workload Automation Tool
A workload automation tool is a platform that centrally manages the
execution of processes across different systems. It provides a single
point of control for planning, execution and monitoring of various
asynchronous applications that run in a complex and diverse
environment.

17
Cross Platform
Native
Connectors
Centralized
Control
Impact Modeling
Dependency
Tracking
Job
Monitoring
Process Scheduling
Global Policies

19
Centrally control the entire data ﬂow from
jobs down to infrastructure, giving us
complete control over cost, service
windows and data governance

20
Reporting & Data
ETL Jobs
Big Data Applications
Elastic Infrastructure

21
With Control-M we are able to implement…
• Appropriate service models to cut costs
• Post-processing DQM framework
• Service level agreements and notiﬁcations
• Environment mirroring
• Visibility into data readiness

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This document provides an overview of Apache NiFi and dataflow. It begins with an introduction to the challenges of moving data effectively within and between systems. It then discusses Apache NiFi's key features for addressing these challenges, including guaranteed delivery, data buffering, prioritized queuing, and data provenance. The document outlines NiFi's architecture and components like repositories and extension points. It also previews a live demo and invites attendees to further discuss Apache NiFi at a Birds of a Feather session.

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Many Organizations are currently processing various types of data and in different formats. Most often this data will be in free form, As the consumers of this data growing it’s imperative that this free-flowing data needs to adhere to a schema. It will help data consumers to have an expectation of about the type of data they are getting and also they will be able to avoid immediate impact if the upstream source changes its format. Having a uniform schema representation also gives the Data Pipeline a really easy way to integrate and support various systems that use different data formats. SchemaRegistry is a central repository for storing, evolving schemas. It provides an API & tooling to help developers and users to register a schema and consume that schema without having any impact if the schema changed. Users can tag different schemas and versions, register for notifications of schema changes with versions etc. In this talk, we will go through the need for a schema registry and schema evolution and showcase the integration with Apache NiFi, Apache Kafka, Apache Storm.

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DeepLearning is not just a hype - it outperforms state-of-the-art ML algorithms. One by one. In this talk we will show how DeepLearning can be used for detecting anomalies on IoT sensor data streams at high speed using DeepLearning4J on top of different BigData engines like ApacheSpark and ApacheFlink. Key in this talk is the absence of any large training corpus since we are using unsupervised machine learning - a domain current DL research threats step-motherly. As we can see in this demo LSTM networks can learn very complex system behavior - in this case data coming from a physical model simulating bearing vibration data. Once draw back of DeepLearning is that normally a very large labaled training data set is required. This is particularly interesting since we can show how unsupervised machine learning can be used in conjunction with DeepLearning - no labeled data set is necessary. We are able to detect anomalies and predict braking bearings with 10 fold confidence. All examples and all code will be made publicly available and open sources. Only open source components are used.

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QE automation for large systems is a great step forward in increasing system reliability. In the big-data world, multiple components have to come together to provide end-users with business outcomes. This means, that QE Automations scenarios need to be detailed around actual use cases, cross-cutting components. The system tests potentially generate large amounts of data on a recurring basis, verifying which is a tedious job. Given the multiple levels of indirection, the false positives of actual defects are higher, and are generally wasteful. At Hortonworks, we’ve designed and implemented Automated Log Analysis System - Mool, using Statistical Data Science and ML. Currently the work in progress has a batch data pipeline with a following ensemble ML pipeline which feeds into the recommendation engine. The system identifies the root cause of test failures, by correlating the failing test cases, with current and historical error records, to identify root cause of errors across multiple components. The system works in unsupervised mode with no perfect model/stable builds/source-code version to refer to. In addition the system provides limited recommendations to file/open past tickets and compares run-profiles with past runs.

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Backup and Disaster Recovery in Hadoop DataWorks Summit/Hadoop Summit

While you could be tempted assuming data is already safe in a single Hadoop cluster, in practice you have to plan for more. Questions like: "What happens if the entire datacenter fails?, or "How do I recover into a consistent state of data, so that applications can continue to run?" are not a all trivial to answer for Hadoop. Did you know that HDFS snapshots are handling open files not as immutable? Or that HBase snapshots are executed asynchronously across servers and therefore cannot guarantee atomicity for cross region updates (which includes tables)? There is no unified and coherent data backup strategy, nor is there tooling available for many of the included components to build such a strategy. The Hadoop distributions largely avoid this topic as most customers are still in the "single use-case" or PoC phase, where data governance as far as backup and disaster recovery (BDR) is concerned are not (yet) important. This talk first is introducing you to the overarching issue and difficulties of backup and data safety, looking at each of the many components in Hadoop, including HDFS, HBase, YARN, Oozie, the management components and so on, to finally show you a viable approach using built-in tools. You will also learn not to take this topic lightheartedly and what is needed to implement and guarantee a continuous operation of Hadoop cluster based solutions.

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Workload Automation + Hadoop?

1. Workload Automa-on + Hadoop? Oh Yeah! …a Match Made in Heaven

3. The Malwarebytes Story In 2004, around the age of 15, Marcin was trying to download a video game from the internet, like most kids his age. In doing so, he infected his computer with multiple pieces of malware. Feeling let down that his antivirus had failed him, and with the fear of being grounded, he set out to find a solution. After three days of searching, Marcin found a group of individuals online who were helping others like him who had also been infected. They patiently helped him clean his computer, and to Marcin these people were superheroes. It was then he began to teach himself how to code, and wrote the very first version of anti-malware’s remediation code. He gave it out for free. Later Marcin partnered with Bruce Harrison to launch a paid for version of his software with a lifetime license, for only $25.00. In 2008 they cofounded Malwarebytes with a mission to create a malware-free existence for the world. Since then, Malwarebytes has been downloaded over 500 million times worldwide, and has removed over 5 billion infections.

4. Today, Malwarebytes has grown to more than 425 employees in 15 countries, and has offices in California, Florida, Ireland, and Estonia. Malwarebytes has also become a world leader in remediation and malware prevention. Malwarebytes is a leader in both the Endpoint Protection Platform (EPP) and Endpoint Detection and Response (EDR) spaces. Marcin Kleczynski Chief Executive Officer Malwarebytes

5. – Our Vision “We believe that everyone has a fundamental right to a malware-free existence”

6. – Our Mission “To create the best disinfection and protection solutions to combat the world’s most harmful threats.”

7. “Everyone has a fundamental right to a malware-free existence” 1st Fastest growing security company over 100m run rate 250k Downloads per day 2.4B Protection events in 2015 500M+ Installs to date

8. “Everyone has a fundamental right to a malware-free existence” 10,000+ Businesses protected 750,000,000 IT hours saved 605,000 Threats blocked per hour 28,000,000+ Endpoints protected

9. Data Science and Engineering @ Malwarebytes Data Engineering Data Operations Data Science & Analytics Application Support

10. 10 Products Customers Threat Marketing Compliance Marketing Customer Success POV Tools

11. 11 Data is Coming Data Science and Engineering

12. “I didn’t come here to learn about Malwarebytes, I like data…”

14. 14 Workload automation?

16. 16 Workload Automation Tool A workload automation tool is a platform that centrally manages the execution of processes across different systems. It provides a single point of control for planning, execution and monitoring of various asynchronous applications that run in a complex and diverse environment.

17. 17 Cross Platform Native Connectors Centralized Control Impact Modeling Dependency Tracking Job Monitoring Process Scheduling Global Policies

18. 18 The Vision.

19. 19 Centrally control the entire data ﬂow from jobs down to infrastructure, giving us complete control over cost, service windows and data governance

20. 20 Reporting & Data ETL Jobs Big Data Applications Elastic Infrastructure

21. 21 With Control-M we are able to implement… • Appropriate service models to cut costs • Post-processing DQM framework • Service level agreements and notiﬁcations • Environment mirroring • Visibility into data readiness

22. 22

23. 23 Shall we chat?

Workload Automation + Hadoop?

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