This Edureka Apache Spark Interview Questions and Answers tutorial helps you in understanding how to tackle questions in a Spark interview and also gives you an idea of the questions that can be asked in a Spark Interview. The Spark interview questions cover a wide range of questions from various Spark components. Below are the topics covered in this tutorial:
1. Basic Questions
2. Spark Core Questions
3. Spark Streaming Questions
4. Spark GraphX Questions
5. Spark MLlib Questions
6. Spark SQL Questions
Apache Spark Training | Spark Tutorial For Beginners | Apache Spark Certifica...Edureka!
This Edureka "Apache Spark Training" tutorial will talk about how Apache Spark works practically. We have demonstrated a Movie Recommendation Project using Apache Spark in this tutorial. Below are the topics covered in this tutorial:
1) Use Cases Of Real Time Analytics
2) Movie Recommendation System Using Spark
3) What Is Spark?
4) Getting Movie Dataset
5) Spark Streaming
6) Collaborative Filtering
7) Spark MLlib
8) Fetching Results
9) Storing Results
Spark Hadoop Tutorial | Spark Hadoop Example on NBA | Apache Spark Training |...Edureka!
This Edureka Spark Hadoop Tutorial will help you understand how to use Spark and Hadoop together. This Spark Hadoop tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Apache Spark concepts. Below are the topics covered in this tutorial:
1) Spark Overview
2) Hadoop Overview
3) Spark vs Hadoop
4) Why Spark Hadoop?
5) Using Hadoop With Spark
6) Use Case - Sports Analytics (NBA)
Apache Spark Tutorial | Spark Tutorial for Beginners | Apache Spark Training ...Edureka!
This Edureka Spark Tutorial will help you to understand all the basics of Apache Spark. This Spark tutorial is ideal for both beginners as well as professionals who want to learn or brush up Apache Spark concepts. Below are the topics covered in this tutorial:
1) Big Data Introduction
2) Batch vs Real Time Analytics
3) Why Apache Spark?
4) What is Apache Spark?
5) Using Spark with Hadoop
6) Apache Spark Features
7) Apache Spark Ecosystem
8) Demo: Earthquake Detection Using Apache Spark
Pyspark Tutorial | Introduction to Apache Spark with Python | PySpark Trainin...Edureka!
** PySpark Certification Training: https://www.edureka.co/pyspark-certification-training**
This Edureka tutorial on PySpark Tutorial will provide you with a detailed and comprehensive knowledge of Pyspark, how it works, the reason why python works best with Apache Spark. You will also learn about RDDs, data frames and mllib.
This Edureka Apache Spark Interview Questions and Answers tutorial helps you in understanding how to tackle questions in a Spark interview and also gives you an idea of the questions that can be asked in a Spark Interview. The Spark interview questions cover a wide range of questions from various Spark components. Below are the topics covered in this tutorial:
1. Basic Questions
2. Spark Core Questions
3. Spark Streaming Questions
4. Spark GraphX Questions
5. Spark MLlib Questions
6. Spark SQL Questions
Apache Spark Training | Spark Tutorial For Beginners | Apache Spark Certifica...Edureka!
This Edureka "Apache Spark Training" tutorial will talk about how Apache Spark works practically. We have demonstrated a Movie Recommendation Project using Apache Spark in this tutorial. Below are the topics covered in this tutorial:
1) Use Cases Of Real Time Analytics
2) Movie Recommendation System Using Spark
3) What Is Spark?
4) Getting Movie Dataset
5) Spark Streaming
6) Collaborative Filtering
7) Spark MLlib
8) Fetching Results
9) Storing Results
Spark Hadoop Tutorial | Spark Hadoop Example on NBA | Apache Spark Training |...Edureka!
This Edureka Spark Hadoop Tutorial will help you understand how to use Spark and Hadoop together. This Spark Hadoop tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Apache Spark concepts. Below are the topics covered in this tutorial:
1) Spark Overview
2) Hadoop Overview
3) Spark vs Hadoop
4) Why Spark Hadoop?
5) Using Hadoop With Spark
6) Use Case - Sports Analytics (NBA)
Apache Spark Tutorial | Spark Tutorial for Beginners | Apache Spark Training ...Edureka!
This Edureka Spark Tutorial will help you to understand all the basics of Apache Spark. This Spark tutorial is ideal for both beginners as well as professionals who want to learn or brush up Apache Spark concepts. Below are the topics covered in this tutorial:
1) Big Data Introduction
2) Batch vs Real Time Analytics
3) Why Apache Spark?
4) What is Apache Spark?
5) Using Spark with Hadoop
6) Apache Spark Features
7) Apache Spark Ecosystem
8) Demo: Earthquake Detection Using Apache Spark
Pyspark Tutorial | Introduction to Apache Spark with Python | PySpark Trainin...Edureka!
** PySpark Certification Training: https://www.edureka.co/pyspark-certification-training**
This Edureka tutorial on PySpark Tutorial will provide you with a detailed and comprehensive knowledge of Pyspark, how it works, the reason why python works best with Apache Spark. You will also learn about RDDs, data frames and mllib.
Writing Apache Spark and Apache Flink Applications Using Apache BahirLuciano Resende
Big Data is all about being to access and process data in various formats, and from various sources. Apache Bahir provides extensions to distributed analytic platforms providing them access to different data sources. In this talk we will introduce you to Apache Bahir and its various connectors that are available for Apache Spark and Apache Flink. We will also go over the details of how to build, test and deploy an Spark Application using the MQTT data source for the new Apache Spark 2.0 Structure Streaming functionality.
PySpark Programming | PySpark Concepts with Hands-On | PySpark Training | Edu...Edureka!
** PySpark Certification Training: https://www.edureka.co/pyspark-certification-training **
This Edureka tutorial on PySpark Programming will give you a complete insight of the various fundamental concepts of PySpark. Fundamental concepts include the following:
1. PySpark
2. RDDs
3. DataFrames
4. PySpark SQL
5. PySpark Streaming
6. Machine Learning (MLlib)
PySpark Training | PySpark Tutorial for Beginners | Apache Spark with Python ...Edureka!
** PySpark Certification Training: https://www.edureka.co/pyspark-certification-training **
This Edureka tutorial on PySpark Training will help you learn about PySpark API. You will get to know how python can be used with Apache Spark for Big Data Analytics. Edureka's structured training on Pyspark will help you master skills that are required to become a successful Spark Developer using Python and prepare you for the Cloudera Hadoop and Spark Developer Certification Exam (CCA175).
In this era of ever growing data, the need for analyzing it for meaningful business insights becomes more and more significant. There are different Big Data processing alternatives like Hadoop, Spark, Storm etc. Spark, however is unique in providing batch as well as streaming capabilities, thus making it a preferred choice for lightening fast Big Data Analysis platforms.
Improving Python and Spark (PySpark) Performance and InteroperabilityWes McKinney
Slides from Spark Summit East 2017 — February 9, 2017 in Boston. Discusses ongoing development work to accelerate Python-on-Spark performance using Apache Arrow and other tools
Machine learning in the enterprise is an iterative process. Data scientists will tweak or replace their learning algorithm in a small data sample until they find an approach that works for the business problem and then apply the Analytics to the full data set. Apache SystemML is a new system that accelerates this kind of exploratory algorithm development for large-scale machine learning problems. SystemML provides a high-level language to quickly implement and run machine learning algorithms on Spark. SystemML’s cost-based optimizer takes care of low-level decisions about how to use Spark’s parallelism, allowing users to focus on the algorithm and the real-world problem that the algorithm is trying to solve. This talk will introduce you to SystemML and get you started building declarative analytics with SystemML using a simple Zeppelin notebook and running on Apache Spark environment.
In the past, emerging technologies took years to mature. In the case of big data, while effective tools are still emerging, the analytics requirements are changing rapidly resulting in businesses to either make it or be left behind
Paris Spark Meetup Oct 26, 2015 - Spark After Dark v1.5 - Best of Advanced Ap...Chris Fregly
* Title *
Spark After Dark 1.5: Deep Dive Into Latest Perf and Scale Improvements in Spark Ecosystem
* Abstract *
Combining the most popular and technically-deep material from his wildly popular Advanced Apache Spark Meetup, Chris Fregly will provide code-level deep dives into the latest performance and scalability advancements within the Apache Spark Ecosystem by exploring the following:
1) Building a Scalable and Performant Spark SQL/DataFrames Data Source Connector such as Spark-CSV, Spark-Cassandra, Spark-ElasticSearch, and Spark-Redshift
2) Speeding Up Spark SQL Queries using Partition Pruning and Predicate Pushdowns with CSV, JSON, Parquet, Avro, and ORC
3) Tuning Spark Streaming Performance and Fault Tolerance with KafkaRDD and KinesisRDD
4) Maintaining Stability during High Scale Streaming Ingestion using Approximations and Probabilistic Data Structures from Spark, Redis, and Twitter's Algebird
5) Building Effective Machine Learning Models using Feature Engineering, Dimension Reduction, and Natural Language Processing with MLlib/GraphX, ML Pipelines, DIMSUM, Locality Sensitive Hashing, and Stanford's CoreNLP
6) Tuning Core Spark Performance by Acknowledging Mechanical Sympathy for the Physical Limitations of OS and Hardware Resources such as CPU, Memory, Network, and Disk with Project Tungsten, Asynchronous Netty, and Linux epoll
* Demos *
This talk features many interesting and audience-interactive demos - as well as code-level deep dives into many of the projects listed above.
All demo code is available on Github at the following link: https://github.com/fluxcapacitor/pipeline/wiki
In addition, the entire demo environment has been Dockerized and made available for download on Docker Hub at the following link: https://hub.docker.com/r/fluxcapacitor/pipeline/
* Speaker Bio *
Chris Fregly is a Principal Data Solutions Engineer for the newly-formed IBM Spark Technology Center, an Apache Spark Contributor, a Netflix Open Source Committer, as well as the Organizer of the global Advanced Apache Spark Meetup and Author of the Upcoming Book, Advanced Spark.
Previously, Chris was a Data Solutions Engineer at Databricks and a Streaming Data Engineer at Netflix.
When Chris isn’t contributing to Spark and other open source projects, he’s creating book chapters, slides, and demos to share knowledge with his peers at meetups and conferences throughout the world.
This is the second session of the learning pathway at PASS Summit 2019, which is still a stand alone session to teach you how to write proper Linux BASH scripts
Consistent, robust error management is a critical feature of any successful application. Developers need to know all that is possible in PL/SQL regarding the raising, handling and logging of errors, and that standardize the way in which those tasks are performed. This presentation takes you beyond the basics of exception handling in PL/SQL to explore the wide range of specialized error management features in Oracle. We will cover FORALL's SAVE EXCEPTIONS, DML error logging with the DBMS_ERRLOG package, the AFTERSERVERERROR trigger, the DBMS_UTILITY.FORMAT_ERROR_BACKTRACE function, and more. Use this material to help you fully leverage PL/SQL error management features, making it easier to identify the sources of problems and fix them more rapidly. https://oracle.com/plsql
ROCm and Distributed Deep Learning on Spark and TensorFlowDatabricks
ROCm, the Radeon Open Ecosystem, is an open-source software foundation for GPU computing on Linux. ROCm supports TensorFlow and PyTorch using MIOpen, a library of highly optimized GPU routines for deep learning. In this talk, we describe how Apache Spark is a key enabling platform for distributed deep learning on ROCm, as it enables different deep learning frameworks to be embedded in Spark workflows in a secure end-to-end machine learning pipeline. We will analyse the different frameworks for integrating Spark with Tensorflow on ROCm, from Horovod to HopsML to Databrick's Project Hydrogen. We will also examine the surprising places where bottlenecks can surface when training models (everything from object stores to the Data Scientists themselves), and we will investigate ways to get around these bottlenecks. The talk will include a live demonstration of training and inference for a Tensorflow application embedded in a Spark pipeline written in a Jupyter notebook on Hopsworks with ROCm.
Spark Streaming API walk-through and insights of the dynamics of how it works. Presented at the Spark Belgium Meetup. (Presentation included live demo on backpressure)
Writing Apache Spark and Apache Flink Applications Using Apache BahirLuciano Resende
Big Data is all about being to access and process data in various formats, and from various sources. Apache Bahir provides extensions to distributed analytic platforms providing them access to different data sources. In this talk we will introduce you to Apache Bahir and its various connectors that are available for Apache Spark and Apache Flink. We will also go over the details of how to build, test and deploy an Spark Application using the MQTT data source for the new Apache Spark 2.0 Structure Streaming functionality.
PySpark Programming | PySpark Concepts with Hands-On | PySpark Training | Edu...Edureka!
** PySpark Certification Training: https://www.edureka.co/pyspark-certification-training **
This Edureka tutorial on PySpark Programming will give you a complete insight of the various fundamental concepts of PySpark. Fundamental concepts include the following:
1. PySpark
2. RDDs
3. DataFrames
4. PySpark SQL
5. PySpark Streaming
6. Machine Learning (MLlib)
PySpark Training | PySpark Tutorial for Beginners | Apache Spark with Python ...Edureka!
** PySpark Certification Training: https://www.edureka.co/pyspark-certification-training **
This Edureka tutorial on PySpark Training will help you learn about PySpark API. You will get to know how python can be used with Apache Spark for Big Data Analytics. Edureka's structured training on Pyspark will help you master skills that are required to become a successful Spark Developer using Python and prepare you for the Cloudera Hadoop and Spark Developer Certification Exam (CCA175).
In this era of ever growing data, the need for analyzing it for meaningful business insights becomes more and more significant. There are different Big Data processing alternatives like Hadoop, Spark, Storm etc. Spark, however is unique in providing batch as well as streaming capabilities, thus making it a preferred choice for lightening fast Big Data Analysis platforms.
Improving Python and Spark (PySpark) Performance and InteroperabilityWes McKinney
Slides from Spark Summit East 2017 — February 9, 2017 in Boston. Discusses ongoing development work to accelerate Python-on-Spark performance using Apache Arrow and other tools
Machine learning in the enterprise is an iterative process. Data scientists will tweak or replace their learning algorithm in a small data sample until they find an approach that works for the business problem and then apply the Analytics to the full data set. Apache SystemML is a new system that accelerates this kind of exploratory algorithm development for large-scale machine learning problems. SystemML provides a high-level language to quickly implement and run machine learning algorithms on Spark. SystemML’s cost-based optimizer takes care of low-level decisions about how to use Spark’s parallelism, allowing users to focus on the algorithm and the real-world problem that the algorithm is trying to solve. This talk will introduce you to SystemML and get you started building declarative analytics with SystemML using a simple Zeppelin notebook and running on Apache Spark environment.
In the past, emerging technologies took years to mature. In the case of big data, while effective tools are still emerging, the analytics requirements are changing rapidly resulting in businesses to either make it or be left behind
Paris Spark Meetup Oct 26, 2015 - Spark After Dark v1.5 - Best of Advanced Ap...Chris Fregly
* Title *
Spark After Dark 1.5: Deep Dive Into Latest Perf and Scale Improvements in Spark Ecosystem
* Abstract *
Combining the most popular and technically-deep material from his wildly popular Advanced Apache Spark Meetup, Chris Fregly will provide code-level deep dives into the latest performance and scalability advancements within the Apache Spark Ecosystem by exploring the following:
1) Building a Scalable and Performant Spark SQL/DataFrames Data Source Connector such as Spark-CSV, Spark-Cassandra, Spark-ElasticSearch, and Spark-Redshift
2) Speeding Up Spark SQL Queries using Partition Pruning and Predicate Pushdowns with CSV, JSON, Parquet, Avro, and ORC
3) Tuning Spark Streaming Performance and Fault Tolerance with KafkaRDD and KinesisRDD
4) Maintaining Stability during High Scale Streaming Ingestion using Approximations and Probabilistic Data Structures from Spark, Redis, and Twitter's Algebird
5) Building Effective Machine Learning Models using Feature Engineering, Dimension Reduction, and Natural Language Processing with MLlib/GraphX, ML Pipelines, DIMSUM, Locality Sensitive Hashing, and Stanford's CoreNLP
6) Tuning Core Spark Performance by Acknowledging Mechanical Sympathy for the Physical Limitations of OS and Hardware Resources such as CPU, Memory, Network, and Disk with Project Tungsten, Asynchronous Netty, and Linux epoll
* Demos *
This talk features many interesting and audience-interactive demos - as well as code-level deep dives into many of the projects listed above.
All demo code is available on Github at the following link: https://github.com/fluxcapacitor/pipeline/wiki
In addition, the entire demo environment has been Dockerized and made available for download on Docker Hub at the following link: https://hub.docker.com/r/fluxcapacitor/pipeline/
* Speaker Bio *
Chris Fregly is a Principal Data Solutions Engineer for the newly-formed IBM Spark Technology Center, an Apache Spark Contributor, a Netflix Open Source Committer, as well as the Organizer of the global Advanced Apache Spark Meetup and Author of the Upcoming Book, Advanced Spark.
Previously, Chris was a Data Solutions Engineer at Databricks and a Streaming Data Engineer at Netflix.
When Chris isn’t contributing to Spark and other open source projects, he’s creating book chapters, slides, and demos to share knowledge with his peers at meetups and conferences throughout the world.
This is the second session of the learning pathway at PASS Summit 2019, which is still a stand alone session to teach you how to write proper Linux BASH scripts
Consistent, robust error management is a critical feature of any successful application. Developers need to know all that is possible in PL/SQL regarding the raising, handling and logging of errors, and that standardize the way in which those tasks are performed. This presentation takes you beyond the basics of exception handling in PL/SQL to explore the wide range of specialized error management features in Oracle. We will cover FORALL's SAVE EXCEPTIONS, DML error logging with the DBMS_ERRLOG package, the AFTERSERVERERROR trigger, the DBMS_UTILITY.FORMAT_ERROR_BACKTRACE function, and more. Use this material to help you fully leverage PL/SQL error management features, making it easier to identify the sources of problems and fix them more rapidly. https://oracle.com/plsql
ROCm and Distributed Deep Learning on Spark and TensorFlowDatabricks
ROCm, the Radeon Open Ecosystem, is an open-source software foundation for GPU computing on Linux. ROCm supports TensorFlow and PyTorch using MIOpen, a library of highly optimized GPU routines for deep learning. In this talk, we describe how Apache Spark is a key enabling platform for distributed deep learning on ROCm, as it enables different deep learning frameworks to be embedded in Spark workflows in a secure end-to-end machine learning pipeline. We will analyse the different frameworks for integrating Spark with Tensorflow on ROCm, from Horovod to HopsML to Databrick's Project Hydrogen. We will also examine the surprising places where bottlenecks can surface when training models (everything from object stores to the Data Scientists themselves), and we will investigate ways to get around these bottlenecks. The talk will include a live demonstration of training and inference for a Tensorflow application embedded in a Spark pipeline written in a Jupyter notebook on Hopsworks with ROCm.
Spark Streaming API walk-through and insights of the dynamics of how it works. Presented at the Spark Belgium Meetup. (Presentation included live demo on backpressure)
Squeezing Deep Learning Into Mobile PhonesAnirudh Koul
A practical talk by Anirudh Koul aimed at how to run Deep Neural Networks to run on memory and energy constrained devices like smart phones. Highlights some frameworks and best practices.
Double Your Hadoop Hardware Performance with SmartSenseHortonworks
Hortonworks SmartSense provides proactive recommendations that improve cluster performance, security and operations. And since 30% of issues are configuration related, Hortonworks SmartSense makes an immediate impact on Hadoop system performance and availability, in some cases boosting hardware performance by two times. Learn how SmartSense can help you increase the efficiency of your Hadoop hardware, through customized cluster recommendations.
View the on-demand webinar: https://hortonworks.com/webinar/boosts-hadoop-hardware-performance-2x-smartsense/
Building Streaming And Fast Data Applications With Spark, Mesos, Akka, Cassan...Lightbend
It’s become clear to many business that the ability to extract real-time actionable insights from data is not only a source of competitive advantage, but also a way to defend their existing business models from disruption. So while legacy models such as nightly batch jobs aren’t disappearing, an era of fast, streaming data (aka “Fast Data”) is upon us, and represents the state of the art for gaining real-time perishable insights that can then be used to serve existing customers better, acquiring new markets and keep the competition at bay.
That said, distributed, Fast Data architectures are much harder to build, and carry their own set of challenges. Enterprises looking to move quickly are presented with a growing ecosystem of technologies, which often delays fast decisions and provides its own set of risks:
* With so many choices, what tools should you use?
* How do you avoid making rookie mistakes?
* What are the best patterns and practices for streaming applications?
In this webinar with Sean Glover, Senior Consultant at Lightbend and industry veteran, we examine the rise of streaming systems built around Spark, Mesos, Akka, Cassandra and Kafka, their role in handling endless streams of data to gain real-time insights. Sean then reviews how the Lightbend Fast Data Platform (FDP) brings them together in a comprehensive, easy to use, integrated platform, which includes installation, integration, and monitoring tools tuned for various deployment scenarios, plus sample applications.
Strata+Hadoop 2017 San Jose: Lessons from a year of supporting Apache Kafkaconfluent
The number of deployments of Apache Kafka at enterprise scale has greatly increased in the years since Kafka’s original development in 2010. Along with this rapid growth has come a wide variety of use cases and deployment strategies that transcend what Kafka’s creators imagined when they originally developed the technology. As the scope and reach of streaming data platforms based on Apache Kafka has grown, the need to understand monitoring and troubleshooting strategies has as well.
Dustin Cote and Ryan Pridgeon share their experience supporting Apache Kafka at enterprise-scale and explore monitoring and troubleshooting techniques to help you avoid pitfalls when scaling large-scale Kafka deployments.
Topics include:
- Effective use of JMX for Kafka
- Tools for preventing small problems from becoming big ones
- Efficient architectures proven in the wild
- Finding and storing the right information when it all goes wrong
Visit www.confluent.io for more information.
Explore IoT in Big Data while brewing beer. All verticals are instrumenting devices to learn more about their process to help cut costs or improve efficiency.
Structuring Apache Spark 2.0: SQL, DataFrames, Datasets And Streaming - by Mi...Databricks
“As Apache Spark becomes more widely adopted, we have focused on creating higher-level APIs that provide increased opportunities for automatic optimization. In this talk, I give an overview of some of the exciting new API’s available in Spark 2.0, namely Datasets and Structured Streaming. Together, these APIs are bringing the power of Catalyst, Spark SQL's query optimizer, to all users of Spark. I'll focus on specific examples of how developers can build their analyses more quickly and efficiently simply by providing Spark with more information about what they are trying to accomplish.” - Michael
Databricks Blog: "Deep Dive into Spark SQL’s Catalyst Optimizer"
https://databricks.com/blog/2015/04/13/deep-dive-into-spark-sqls-catalyst-optimizer.html
// About the Presenter //
Michael Armbrust is the lead developer of the Spark SQL project at Databricks. He received his PhD from UC Berkeley in 2013, and was advised by Michael Franklin, David Patterson, and Armando Fox. His thesis focused on building systems that allow developers to rapidly build scalable interactive applications, and specifically defined the notion of scale independence. His interests broadly include distributed systems, large-scale structured storage and query optimization.
Follow Michael on -
Twitter: https://twitter.com/michaelarmbrust
LinkedIn: https://www.linkedin.com/in/michaelarmbrust
Tuning and Monitoring Deep Learning on Apache SparkDatabricks
Deep Learning on Apache Spark has the potential for huge impact in research and industry. This talk will describe best practices for building deep learning pipelines with Spark.
Rather than comparing deep learning systems or specific optimizations, this talk will focus on issues that are common to many deep learning frameworks when running on a Spark cluster: optimizing cluster setup and data ingest, tuning the cluster, and monitoring long-running jobs. We will demonstrate the techniques we cover using Google’s popular TensorFlow library.
More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters. Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput. Interactive monitoring facilitates both the work of configuration and checking the stability of deep learning jobs.
Speaker: Tim Hunter
This talk was originally presented at Spark Summit East 2017.
Apache Spark: The Analytics Operating SystemAdarsh Pannu
This presentation was delivered by Adarsh Pannu at IBM's Insight Conference in Nov 2015. For a recording, visit: https://www.youtube.com/watch?v=Tbm7HIlmwJQ
The presentation provides an overview of Apache Spark, a general-purpose big data processing engine built around speed, ease of use and sophisticated analytics. It enumerates the benefits of incorporating Spark in the enterprise, including how it allows developers to write fully-featured distributed applications ranging from traditional data processing pipelines to complex machine learning. The presentation uses the Airline "On Time" data set to explore various components of the Spark stack.
Spark Tuning For Enterprise System Administrators, Spark Summit East 2016Anya Bida
by Anya Bida and Rachel Warren from Alpine Data
https://spark-summit.org/east-2016/events/spark-tuning-for-enterprise-system-administrators/
Spark offers the promise of speed, but many enterprises are reluctant to make the leap from Hadoop to Spark. Indeed, System Administrators will face many challenges with tuning Spark performance. This talk is a gentle introduction to Spark Tuning for the Enterprise System Administrator, based on experience assisting two enterprise companies running Spark in yarn-cluster mode. The initial challenges can be categorized in two FAQs. First, with so many Spark Tuning parameters, how do I know which parameters are important for which jobs? Second, once I know which Spark Tuning parameters I need, how do I enforce them for the various users submitting various jobs to my cluster? This introduction to Spark Tuning will enable enterprise system administrators to overcome common issues quickly and focus on more advanced Spark Tuning challenges. The audience will understand the “cheat-sheet” posted here: http://techsuppdiva.github.io/ Key takeaways: FAQ 1: With so many Spark Tuning parameters, how do I know which parameters are important for which jobs? Solution 1: The Spark Tuning cheat-sheet! A visualization that guides the System Administrator to quickly overcome the most common hurdles to algorithm deployment. [1]http://techsuppdiva.github.io/ FAQ 2: Once I know which Spark Tuning parameters I need, how do I enforce them at the user level? job level? algorithm level? project level? cluster level? Solution 2: We’ll approach these challenges using job & cluster configuration, the Spark context, and 3rd party tools – of which Alpine will be one example. We’ll operationalize Spark parameters according to user, job, algorithm, workflow pipeline, or cluster levels.
Netflix Recommendations Using Spark + Cassandra (Prasanna Padmanabhan & Roopa...DataStax
Learning is an analytic process of exploring the past in order to predict the future. Hence, being able to travel back in time to create features is critical for machine learning projects to be successful. To enable this, we built a time machine that computes features for any arbitrary time in the recent past for offline experimentation. We also built a real-time stream processing system to capture the interests of members during different times of the day and to quickly adapt to changes in the collective interests of members as it happens in case of real-world events.
Building the time machine for offline experimentation and the real-time infrastructure for online recommendations with Apache Spark (Streaming) and Apache Cassandra empowered us to both scale up the data size by an order of magnitude and train and validate the models in less time. We will delve into the architecture, use case details, data models used for cassandra and share our learnings.
About the Speakers
Prasanna Padmanabhan Engineering Manager, Netflix
Prasanna leads the Data Systems for Personalization team at Netflix. His primary focus is on building various big data infrastructure components that help their algorithmic engineers to innovate faster and improve personalization for Netflix members. In the past, he has built distributed data systems that leverages both batch and stream processing.
Roopa Tangirala Engineering Manager, Netflix
Roopa Tangirala is an experienced engineering leader with extensive background in databases, be they distributed or relational. She manages the database engineering team at Netflix responsible for operating cloud persistent and semipersistent runtime stores for Netflix, which includes Cassandra, Elasticsearch, Dynomite and MySQL databases, by ensuring data availability, durability, and scalability to meet the growing business needs.
What is Apache Spark | Apache Spark Tutorial For Beginners | Apache Spark Tra...Edureka!
This Edureka "What is Spark" tutorial will introduce you to big data analytics framework - Apache Spark. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Apache Spark concepts. Below are the topics covered in this tutorial:
1) Big Data Analytics
2) What is Apache Spark?
3) Why Apache Spark?
4) Using Spark with Hadoop
5) Apache Spark Features
6) Apache Spark Architecture
7) Apache Spark Ecosystem - Spark Core, Spark Streaming, Spark MLlib, Spark SQL, GraphX
8) Demo: Analyze Flight Data Using Apache Spark
Fast Data: A Customer’s Journey to Delivering a Compelling Real-Time SolutionGuido Schmutz
This is my part of the Open World 2014 presentation on Fast Data and Oracle Event Processing (OEP) 12c.
It contains an architecture discussion with some architecture patterns of where Events are useful. The 2nd part is a demo showcase showing OEP12c and BAM12c in action, analyzing the live OOW2014 twitter feed.
Scrum/XP using Team System (devLink & Agile 2009)Tommy Norman
This is the slide deck from my devLink 09 and Agile 2009 conference presentations. I skipped the Scrum intro slides at Agile 2009 since most of the crowd already had the basics down. This was nainly a demo so for over half the presentation I was not using slides.
Agile roundabout 2017 01 - keeping your ci-cd system as fast as it needs to beAbraham Marin-Perez
Short version of my talk on how to keep CI/CD pipelines as fast as needed. This presentation delves into why fast build pipelines are important and explores different approaches to achieve and measure this.
Apache Spark already has a vectorization optimization in many operations, for instance, internal columnar format, Parquet/ORC vectorized read, Pandas UDFs, etc. Vectorization improves performance greatly in general. In this talk, the performance aspect of SparkR will be discussed and vectorization in SparkR will be introduced with technical details. SparkR vectorization allows users to use the existing codes as are but boost the performance around several thousand present faster when they execute R native functions or convert Spark DataFrame to/from R DataFrame.
Just Enough DevOps for Data Scientists Part II: Handling Infra Failures When ...Anya Bida
Abstract: Imagine we have Ada, our data science intern. Let's run through a very simple wordcount spark job, and find a handful of potential failure points. Dozens of failures can and should happen when running spark jobs on commodity hardware. Given the basic foundation for infrastructure-level expectations, this talk gives Ada tools to ensure her job isn’t caught dead. Once the simple example job runs reliably, with the potential to scale, our data scientist can apply the same toolset to focus on some more interesting algorithms. Turn SNAFUs into successes by anticipating and handling Infra failures gracefully.
Note: this talk is a spark-focused extension of Part I, "Just Enough DevOps For Data Scientists" from Scale by The Bay 2018
https://www.youtube.com/watch?v=RqpnBl5NgW0&t=19s
Bio: Anya Bida (https://www.linkedin.com/in/anyabida/)
Introduction to Spark Streaming & Apache Kafka | Big Data Hadoop Spark Tutori...CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2L6bZbn
This CloudxLab Introduction to Spark Streaming & Apache Kafka tutorial helps you to understand Spark Streaming and Kafka in detail. Below are the topics covered in this tutorial:
1) Spark Streaming - Workflow
2) Use Cases - E-commerce, Real-time Sentiment Analysis & Real-time Fraud Detection
3) Spark Streaming - DStream
4) Word Count Hands-on using Spark Streaming
5) Spark Streaming - Running Locally Vs Running on Cluster
6) Introduction to Apache Kafka
7) Apache Kafka Hands-on on CloudxLab
8) Integrating Spark Streaming & Kafka
9) Spark Streaming & Kafka Hands-on
Spark 2.0 is a major release of Apache Spark. This release has brought many changes to API(s) and libraries of Spark. So in this KnolX, we will be looking at some improvements that are made in Spark 2.0. Also, in these slides we will be getting an introduction to some new features in Spark 2,0 like SparkSession API and Structured Streaming.
Just enough DevOps for Data Scientists (Part II)Databricks
Imagine we have Ada, our data science intern. Let's run through a very simple wordcount spark job, and find a handful of potential failure points. Dozens of failures can and should happen when running spark jobs on commodity hardware. Given the basic foundation for infrastructure-level expectations, this talk gives Ada tools to ensure her job isn’t caught dead. Once the simple example job runs reliably, with the potential to scale, our data scientist can apply the same toolset to focus on some more interesting algorithms. Turn SNAFUs into successes by anticipating and handling Infra failures gracefully.
Note: this talk is a spark-focused extension of Part I, "Just Enough DevOps For Data Scientists" from Scale by The Bay 2018
https://www.youtube.com/watch?v=RqpnBl5NgW0&t=19s
A complete introduction of the Apache Spark, a lightning fast, easy-to-use, and high-flexible large data processing engine. Due to its speed, scalability, and ease of use, wildlife is popular with data scientists.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.