Dan Lynn (AgilData) & Patrick Russell (Craftsy) present on how to do data science in the real world. We discuss data cleansing, ETL, pipelines, hosting, and share several tools used in the industry.
http://www.agildata.com/agildata-hosts-big-data-meetup-featuring-apache-spark/
Slides for talks given at the Denver Java Users Group, Boulder Java Users Group, Denver/Boulder Big Data Users Group.
Dan and Andy will spend an evening rolling up our sleeves with you to try out some real-world use cases for Apache Spark.
We’ll cover Spark’s RDD API, the DataFrame API, as well as the brand new Dataset API.
Introduction to Apache Spark Workshop at Lambda World 2015 on October 23th and 24th, 2015, celebrated in Cádiz. Speakers: @fperezp and @juanpedromoreno
Github Repo: https://github.com/47deg/spark-workshop
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
This lecture was intended to provide an introduction to Apache Spark's features and functionality and importance of Spark as a distributed data processing framework compared to Hadoop MapReduce. The target audience was MSc students with programming skills at beginner to intermediate level.
http://www.agildata.com/agildata-hosts-big-data-meetup-featuring-apache-spark/
Slides for talks given at the Denver Java Users Group, Boulder Java Users Group, Denver/Boulder Big Data Users Group.
Dan and Andy will spend an evening rolling up our sleeves with you to try out some real-world use cases for Apache Spark.
We’ll cover Spark’s RDD API, the DataFrame API, as well as the brand new Dataset API.
Introduction to Apache Spark Workshop at Lambda World 2015 on October 23th and 24th, 2015, celebrated in Cádiz. Speakers: @fperezp and @juanpedromoreno
Github Repo: https://github.com/47deg/spark-workshop
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
This lecture was intended to provide an introduction to Apache Spark's features and functionality and importance of Spark as a distributed data processing framework compared to Hadoop MapReduce. The target audience was MSc students with programming skills at beginner to intermediate level.
Have you been in the situation where you’re about to start a new project and ask yourself, what’s the right tool for the job here? I’ve been in that situation many times and thought it might be useful to share with you a recent project we did and why we selected Spark, Python, and Parquet. My plan is take you through a use case that involves loading, transforming, aggregating, and persisting the dataset. We’ll use an open dataset consisting of full fund holdings graciously provided by Morningstar. My goal in presenting this use case are to have the audience learn about how these technologies can be applied to a real world problem and to inspire members of the audience to start learning these technologies and applying them to their own projects.
When learning Apache Spark, where should a person begin? What are the key fundamentals when learning Apache Spark? Resilient Distributed Datasets, Spark Drivers and Context, Transformations, Actions.
Apache Spark: The Next Gen toolset for Big Data Processingprajods
The Spark project from Apache(spark.apache.org), is the next generation of Big Data processing systems. It uses a new architecture and in-memory processing for orders of magnitude improvement in performance. Some would call it the successor to the Hadoop set of tools. Hadoop is a batch mode Big Data processor and depends on disk based files. Spark improves on this and supports real time and interactive processing, in addition to batch processing.
Table of contents:
1. The Big Data triangle
2. Hadoop stack and its limitations
3. Spark: An Overview
3.a. Spark Streaming
3.b. GraphX: Graph processing
3.c. MLib: Machine Learning
4. Performance characteristics of Spark
What No One Tells You About Writing a Streaming App: Spark Summit East talk b...Spark Summit
So you know you want to write a streaming app but any non-trivial streaming app developer would have to think about these questions:
How do I manage offsets?
How do I manage state?
How do I make my spark streaming job resilient to failures? Can I avoid some failures?
How do I gracefully shutdown my streaming job?
How do I monitor and manage (e.g. re-try logic) streaming job?
How can I better manage the DAG in my streaming job?
When to use checkpointing and for what? When not to use checkpointing?
Do I need a WAL when using streaming data source? Why? When don’t I need one?
In this talk, we’ll share practices that no one talks about when you start writing your streaming app, but you’ll inevitably need to learn along the way.
Migrating from Redshift to Spark at Stitch Fix: Spark Summit East talk by Sky...Spark Summit
Data scientists write SQL queries everyday. Very often they know how to write correct queries but don’t know why their queries are slow. This is more obvious in Spark than in Redshift as Spark requires additional tuning such as caching while Redshift does heavy lifting behind the scene.
In this talk I will cover a few lessons we learned from migrating one of the biggest table here (900M+ rows/day) from AWS Redshift to Spark.
Specifically:
– Why and how do we migrate?
– How do we tune the query for Spark to gain 10x speed vs direct translated from Redshift
– How do we scale the team on Spark (with 80+ people in our data science team)
Apache Spark is rapidly emerging as the prime platform for advanced analytics in Hadoop. This briefing is updated to reflect news and announcements as of July 2014.
Apache Spark Usage in the Open Source EcosystemDatabricks
Apache Spark is an active member of the broad open source community beyond the Apache Foundation. Every day thousands of users combine capabilities of Spark with other open source software to get their job done. This is not by chance. Spark has been designed to behave well with existing ecosystems. For example, PySpark is designed to work well with Pandas, Numpy and other python packages. In this talk we will present an analysis of libraries and open source tools that are commonly used along with Spark in JVM, Python and R ecosystems. Our quantitative results are based on usage of thousands of Spark users. We will show the Spark Summit attendees what the rest of their community finds useful to complement the power of Spark and what parts of Spark API is used in conjunction with most popular open source libraries.
Using SparkR to Scale Data Science Applications in Production. Lessons from t...Spark Summit
R is a hugely popular platform for Data Scientists to create analytic models in many different domains. But when these applications should move from the science lab to the production environment of large enterprises a new set of challenges arises. Independently of R, Spark has been very successful as a powerful general-purpose computing platform. With the introduction of SparkR an exciting new option to productionize Data Science applications has been made available. This talk will give insight into two real-life projects at major enterprises where Data Science applications in R have been migrated to SparkR.
• Dealing with platform challenges: R was not installed on the cluster. We show how to execute SparkR on a Yarn cluster with a dynamic deployment of R.
• Integrating Data Engineering and Data Science: we highlight the technical and cultural challenges that arise from closely integrating these two different areas.
• Separation of concerns: we describe how to disentangle ETL and data preparation from analytic computing and statistical methods.
• Scaling R with SparkR: we present what options SparkR offers to scale R applications and how we applied them to different areas such as time series forecasting and web analytics.
• Performance Improvements: we will show benchmarks for an R applications that took over 20 hours on a single server/single-threaded setup. With moderate effort we have been able to reduce that number to 15 minutes with SparkR. And we will show how we plan to further reduces this to less than a minute in the future.
• Mixing SparkR, SparkSQL and MLlib: we show how we combined the three different libraries to maximize efficiency.
• Summary and Outlook: we describe what we have learnt so far, what the biggest gaps currently are and what challenges we expect to solve in the short- to mid-term.
Unlocking Your Hadoop Data with Apache Spark and CDH5SAP Concur
Spark/Mesos Seattle Meetup group shares the latest presentation from their recent meetup event on showcasing real world implementations of working with Spark within the context of your Big Data Infrastructure.
Session are demo heavy and slide light focusing on getting your development environments up and running including getting up and running, configuration issues, SparkSQL vs. Hive, etc.
To learn more about the Seattle meetup: http://www.meetup.com/Seattle-Spark-Meetup/members/21698691/
Big Data Processing with Apache Spark 2014mahchiev
Apache Spark™ is a fast and general engine for large-scale data processing. It has gained enormous popularity recently with its speed and ease of use and is currently replacing traditional Hadoop MapReduce. We'll talk about:
1. What is Big Data ?
2. The Map-Reduce paradigm
3. What does Apache Spark do?
4. Finally, we'll make a quick demo
Spark summit 2019 infrastructure for deep learning in apache spark 0425Wee Hyong Tok
In machine learning projects, the preparation of large datasets is a key phase which can be complex and expensive. It was traditionally done by data engineers before the handover to data scientists or ML engineers. They operated in different environments due to the differences in the tools, frameworks and runtimes required in each phase. Spark's support for different types of workloads brought data engineering closer to the downstream activities like machine learning that depended on the data. Unifying data acquisition, preprocessing, training models and batch inferencing under a single platform enabled by Spark not only provided seamless experience between different phases and helped accelerate the end-to-end ML lifecycle but also lowered the TCO in the building, managing the infrastructure to cover different phases. With that, the needs of a shared infrastructure expanded to include specialized hardware like GPUs and support deep learning workloads as well. Spark can effectively make use of such infrastructure as it integrates with popular deep learning frameworks and supports acceleration of deep learning jobs using GPUs. In this talk, we share learnings and experiences in supporting different types of workloads in shared clusters equipped for doing deep learning as well as data engineering. We will cover the following topics: * Considerations for sharing the infrastructure for big data and deep learning in Spark * Deep learning in Spark in clusters with and without GPUs * Differences between distributed data processing and distributed machine learning * Multitenancy and isolation in shared infrastructure.
https://databricks.com/sparkaisummit/north-america/sessions-single-2019?id=97
Python and Bigdata - An Introduction to Spark (PySpark)hiteshnd
An Introduction to Spark. A cluster computing framework to process large quantities of data by leveraging RAM across the cluster. Talk was given at PyBelgaum 2015
Running Emerging AI Applications on Big Data Platforms with Ray On Apache SparkDatabricks
With the rapid evolution of AI in recent years, we need to embrace advanced and emerging AI technologies to gain insights and make decisions based on massive amounts of data. Ray (https://github.com/ray-project/ray) is a fast and simple framework open-sourced by UC Berkeley RISELab particularly designed for easily building advanced AI applications in a distributed fashion.
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...Databricks
At the end of day, the only thing that data scientists want is tabular data for their analysis. They do not want to spend hours or days preparing data. How does a data engineer handle the massive amount of data that is being streamed at them from IoT devices and apps, and at the same time add structure to it so that data scientists can focus on finding insights and not preparing data? By the way, you need to do this within minutes (sometimes seconds). Oh… and there are a lot of other data sources that you need to ingest, and the current providers of data are changing their structure.
GoPro has massive amounts of heterogeneous data being streamed from their consumer devices and applications, and they have developed the concept of “dynamic DDL” to structure their streamed data on the fly using Spark Streaming, Kafka, HBase, Hive and S3. The idea is simple: Add structure (schema) to the data as soon as possible; allow the providers of the data to dictate the structure; and automatically create event-based and state-based tables (DDL) for all data sources to allow data scientists to access the data via their lingua franca, SQL, within minutes.
Teaching Apache Spark Clusters to Manage Their Workers Elastically: Spark Sum...Spark Summit
Devops engineers have applied a great deal of creativity and energy to invent tools that automate infrastructure management, in the service of deploying capable and functional applications. For data-driven applications running on Apache Spark, the details of instantiating and managing the backing Spark cluster can be a distraction from focusing on the application logic. In the spirit of devops, automating Spark cluster management tasks allows engineers to focus their attention on application code that provides value to end-users.
Using Openshift Origin as a laboratory, we implemented a platform where Apache Spark applications create their own clusters and then dynamically manage their own scale via host-platform APIs. This makes it possible to launch a fully elastic Spark application with little more than the click of a button.
We will present a live demo of turn-key deployment for elastic Apache Spark applications, and share what we’ve learned about developing Spark applications that manage their own resources dynamically with platform APIs.
The audience for this talk will be anyone looking for ways to streamline their Apache Spark cluster management, reduce the workload for Spark application deployment, or create self-scaling elastic applications. Attendees can expect to learn about leveraging APIs in the Kubernetes ecosystem that enable application deployments to manipulate their own scale elastically.
This slide shows the set of task groups established under the aegis of the RDA/NISO Privacy Implications of Research Data Sets Interest Group; it was used during the NISO Symposium held on September 11, 2016 in conjunction with International Data Week events in Denver, Colorado.
Have you been in the situation where you’re about to start a new project and ask yourself, what’s the right tool for the job here? I’ve been in that situation many times and thought it might be useful to share with you a recent project we did and why we selected Spark, Python, and Parquet. My plan is take you through a use case that involves loading, transforming, aggregating, and persisting the dataset. We’ll use an open dataset consisting of full fund holdings graciously provided by Morningstar. My goal in presenting this use case are to have the audience learn about how these technologies can be applied to a real world problem and to inspire members of the audience to start learning these technologies and applying them to their own projects.
When learning Apache Spark, where should a person begin? What are the key fundamentals when learning Apache Spark? Resilient Distributed Datasets, Spark Drivers and Context, Transformations, Actions.
Apache Spark: The Next Gen toolset for Big Data Processingprajods
The Spark project from Apache(spark.apache.org), is the next generation of Big Data processing systems. It uses a new architecture and in-memory processing for orders of magnitude improvement in performance. Some would call it the successor to the Hadoop set of tools. Hadoop is a batch mode Big Data processor and depends on disk based files. Spark improves on this and supports real time and interactive processing, in addition to batch processing.
Table of contents:
1. The Big Data triangle
2. Hadoop stack and its limitations
3. Spark: An Overview
3.a. Spark Streaming
3.b. GraphX: Graph processing
3.c. MLib: Machine Learning
4. Performance characteristics of Spark
What No One Tells You About Writing a Streaming App: Spark Summit East talk b...Spark Summit
So you know you want to write a streaming app but any non-trivial streaming app developer would have to think about these questions:
How do I manage offsets?
How do I manage state?
How do I make my spark streaming job resilient to failures? Can I avoid some failures?
How do I gracefully shutdown my streaming job?
How do I monitor and manage (e.g. re-try logic) streaming job?
How can I better manage the DAG in my streaming job?
When to use checkpointing and for what? When not to use checkpointing?
Do I need a WAL when using streaming data source? Why? When don’t I need one?
In this talk, we’ll share practices that no one talks about when you start writing your streaming app, but you’ll inevitably need to learn along the way.
Migrating from Redshift to Spark at Stitch Fix: Spark Summit East talk by Sky...Spark Summit
Data scientists write SQL queries everyday. Very often they know how to write correct queries but don’t know why their queries are slow. This is more obvious in Spark than in Redshift as Spark requires additional tuning such as caching while Redshift does heavy lifting behind the scene.
In this talk I will cover a few lessons we learned from migrating one of the biggest table here (900M+ rows/day) from AWS Redshift to Spark.
Specifically:
– Why and how do we migrate?
– How do we tune the query for Spark to gain 10x speed vs direct translated from Redshift
– How do we scale the team on Spark (with 80+ people in our data science team)
Apache Spark is rapidly emerging as the prime platform for advanced analytics in Hadoop. This briefing is updated to reflect news and announcements as of July 2014.
Apache Spark Usage in the Open Source EcosystemDatabricks
Apache Spark is an active member of the broad open source community beyond the Apache Foundation. Every day thousands of users combine capabilities of Spark with other open source software to get their job done. This is not by chance. Spark has been designed to behave well with existing ecosystems. For example, PySpark is designed to work well with Pandas, Numpy and other python packages. In this talk we will present an analysis of libraries and open source tools that are commonly used along with Spark in JVM, Python and R ecosystems. Our quantitative results are based on usage of thousands of Spark users. We will show the Spark Summit attendees what the rest of their community finds useful to complement the power of Spark and what parts of Spark API is used in conjunction with most popular open source libraries.
Using SparkR to Scale Data Science Applications in Production. Lessons from t...Spark Summit
R is a hugely popular platform for Data Scientists to create analytic models in many different domains. But when these applications should move from the science lab to the production environment of large enterprises a new set of challenges arises. Independently of R, Spark has been very successful as a powerful general-purpose computing platform. With the introduction of SparkR an exciting new option to productionize Data Science applications has been made available. This talk will give insight into two real-life projects at major enterprises where Data Science applications in R have been migrated to SparkR.
• Dealing with platform challenges: R was not installed on the cluster. We show how to execute SparkR on a Yarn cluster with a dynamic deployment of R.
• Integrating Data Engineering and Data Science: we highlight the technical and cultural challenges that arise from closely integrating these two different areas.
• Separation of concerns: we describe how to disentangle ETL and data preparation from analytic computing and statistical methods.
• Scaling R with SparkR: we present what options SparkR offers to scale R applications and how we applied them to different areas such as time series forecasting and web analytics.
• Performance Improvements: we will show benchmarks for an R applications that took over 20 hours on a single server/single-threaded setup. With moderate effort we have been able to reduce that number to 15 minutes with SparkR. And we will show how we plan to further reduces this to less than a minute in the future.
• Mixing SparkR, SparkSQL and MLlib: we show how we combined the three different libraries to maximize efficiency.
• Summary and Outlook: we describe what we have learnt so far, what the biggest gaps currently are and what challenges we expect to solve in the short- to mid-term.
Unlocking Your Hadoop Data with Apache Spark and CDH5SAP Concur
Spark/Mesos Seattle Meetup group shares the latest presentation from their recent meetup event on showcasing real world implementations of working with Spark within the context of your Big Data Infrastructure.
Session are demo heavy and slide light focusing on getting your development environments up and running including getting up and running, configuration issues, SparkSQL vs. Hive, etc.
To learn more about the Seattle meetup: http://www.meetup.com/Seattle-Spark-Meetup/members/21698691/
Big Data Processing with Apache Spark 2014mahchiev
Apache Spark™ is a fast and general engine for large-scale data processing. It has gained enormous popularity recently with its speed and ease of use and is currently replacing traditional Hadoop MapReduce. We'll talk about:
1. What is Big Data ?
2. The Map-Reduce paradigm
3. What does Apache Spark do?
4. Finally, we'll make a quick demo
Spark summit 2019 infrastructure for deep learning in apache spark 0425Wee Hyong Tok
In machine learning projects, the preparation of large datasets is a key phase which can be complex and expensive. It was traditionally done by data engineers before the handover to data scientists or ML engineers. They operated in different environments due to the differences in the tools, frameworks and runtimes required in each phase. Spark's support for different types of workloads brought data engineering closer to the downstream activities like machine learning that depended on the data. Unifying data acquisition, preprocessing, training models and batch inferencing under a single platform enabled by Spark not only provided seamless experience between different phases and helped accelerate the end-to-end ML lifecycle but also lowered the TCO in the building, managing the infrastructure to cover different phases. With that, the needs of a shared infrastructure expanded to include specialized hardware like GPUs and support deep learning workloads as well. Spark can effectively make use of such infrastructure as it integrates with popular deep learning frameworks and supports acceleration of deep learning jobs using GPUs. In this talk, we share learnings and experiences in supporting different types of workloads in shared clusters equipped for doing deep learning as well as data engineering. We will cover the following topics: * Considerations for sharing the infrastructure for big data and deep learning in Spark * Deep learning in Spark in clusters with and without GPUs * Differences between distributed data processing and distributed machine learning * Multitenancy and isolation in shared infrastructure.
https://databricks.com/sparkaisummit/north-america/sessions-single-2019?id=97
Python and Bigdata - An Introduction to Spark (PySpark)hiteshnd
An Introduction to Spark. A cluster computing framework to process large quantities of data by leveraging RAM across the cluster. Talk was given at PyBelgaum 2015
Running Emerging AI Applications on Big Data Platforms with Ray On Apache SparkDatabricks
With the rapid evolution of AI in recent years, we need to embrace advanced and emerging AI technologies to gain insights and make decisions based on massive amounts of data. Ray (https://github.com/ray-project/ray) is a fast and simple framework open-sourced by UC Berkeley RISELab particularly designed for easily building advanced AI applications in a distributed fashion.
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...Databricks
At the end of day, the only thing that data scientists want is tabular data for their analysis. They do not want to spend hours or days preparing data. How does a data engineer handle the massive amount of data that is being streamed at them from IoT devices and apps, and at the same time add structure to it so that data scientists can focus on finding insights and not preparing data? By the way, you need to do this within minutes (sometimes seconds). Oh… and there are a lot of other data sources that you need to ingest, and the current providers of data are changing their structure.
GoPro has massive amounts of heterogeneous data being streamed from their consumer devices and applications, and they have developed the concept of “dynamic DDL” to structure their streamed data on the fly using Spark Streaming, Kafka, HBase, Hive and S3. The idea is simple: Add structure (schema) to the data as soon as possible; allow the providers of the data to dictate the structure; and automatically create event-based and state-based tables (DDL) for all data sources to allow data scientists to access the data via their lingua franca, SQL, within minutes.
Teaching Apache Spark Clusters to Manage Their Workers Elastically: Spark Sum...Spark Summit
Devops engineers have applied a great deal of creativity and energy to invent tools that automate infrastructure management, in the service of deploying capable and functional applications. For data-driven applications running on Apache Spark, the details of instantiating and managing the backing Spark cluster can be a distraction from focusing on the application logic. In the spirit of devops, automating Spark cluster management tasks allows engineers to focus their attention on application code that provides value to end-users.
Using Openshift Origin as a laboratory, we implemented a platform where Apache Spark applications create their own clusters and then dynamically manage their own scale via host-platform APIs. This makes it possible to launch a fully elastic Spark application with little more than the click of a button.
We will present a live demo of turn-key deployment for elastic Apache Spark applications, and share what we’ve learned about developing Spark applications that manage their own resources dynamically with platform APIs.
The audience for this talk will be anyone looking for ways to streamline their Apache Spark cluster management, reduce the workload for Spark application deployment, or create self-scaling elastic applications. Attendees can expect to learn about leveraging APIs in the Kubernetes ecosystem that enable application deployments to manipulate their own scale elastically.
This slide shows the set of task groups established under the aegis of the RDA/NISO Privacy Implications of Research Data Sets Interest Group; it was used during the NISO Symposium held on September 11, 2016 in conjunction with International Data Week events in Denver, Colorado.
Accountability Initiative is holding a bar-camp on June 5-6. This bar camp would focus on accountability issues in India. This presentation is being made to facilitate ideas on what can be done in India.
Mobile-led innovations for Direct customer relationshipsRamesh Raman
Extensive media and channel fragmentation and changing demographics have made direct consumer engagement as necessary as it is challenging. However many, otherwise leading, organisations lack the ability to identify and engage with their customers directly
* How can brands innovatively use the exponentially increasing power of mobile and social channels to build a single-view of the consumer?
* How can brands leverage this single-view of a customer to build a direct relationship with them?
Big Data in 200 km/h | AWS Big Data Demystified #1.3 Omid Vahdaty
What we're about
A while ago I entered the challenging world of Big Data. As an engineer, at first, I was not so impressed with this field. As time went by, I realised more and more, The technological challenges in this area are too great to master by one person. Just look at the picture in this articles, it only covers a small fraction of the technologies in the Big Data industry…
Consequently, I created a meetup detailing all the challenges of Big Data, especially in the world of cloud. I am using AWS infrastructure to answer the basic questions of anyone starting their way in the big data world.
how to transform data (TXT, CSV, TSV, JSON) into Parquet, ORCwhich technology should we use to model the data ? EMR? Athena? Redshift? Spectrum? Glue? Spark? SparkSQL?how to handle streaming?how to manage costs?Performance tips?Security tip?Cloud best practices tips?
Some of our online materials:
Website:
https://big-data-demystified.ninja/
Youtube channels:
https://www.youtube.com/channel/UCzeGqhZIWU-hIDczWa8GtgQ?view_as=subscriber
https://www.youtube.com/channel/UCMSdNB0fGmX5dXI7S7Y_LFA?view_as=subscriber
Meetup:
https://www.meetup.com/AWS-Big-Data-Demystified/
https://www.meetup.com/Big-Data-Demystified
Facebook Group :
https://www.facebook.com/groups/amazon.aws.big.data.demystified/
Facebook page (https://www.facebook.com/Amazon-AWS-Big-Data-Demystified-1832900280345700/)
Audience:
Data Engineers
Data Science
DevOps Engineers
Big Data Architects
Solution Architects
CTO
VP R&D
AWS Big Data Demystified #1: Big data architecture lessons learned Omid Vahdaty
AWS Big Data Demystified #1: Big data architecture lessons learned . a quick overview of a big data techonoligies, which were selected and disregard in our company
The video: https://youtu.be/l5KmaZNQxaU
dont forget to subcribe to the youtube channel
The website: https://amazon-aws-big-data-demystified.ninja/
The meetup : https://www.meetup.com/AWS-Big-Data-Demystified/
The facebook group : https://www.facebook.com/Amazon-AWS-Big-Data-Demystified-1832900280345700/
Big Data Pipeline for Analytics at Scale @ FIT CVUT 2014Jaroslav Gergic
The recent boom in big data processing and democratization of the big data space has been enabled by the fact that most of the concepts originated in the research labs of companies such as Google, Amazon, Yahoo and Facebook are now available as open source. Technologies such as Hadoop, Cassandra let businesses around the world to become more data driven and tap into their massive data feeds to mine valuable insights.
At the same time, we are still at a certain stage of the maturity curve of these new big data technologies and of the entire big data technology stack. Many of the technologies originated from a particular use case and attempts to apply them in a more generic fashion are hitting the limits of their technological foundations. In some areas, there are several competing technologies for the same set of use cases, which increases risks and costs of big data implementations.
We will show how GoodData solves the entire big data pipeline today, starting from raw data feeds all the way up to actionable business insights. All this provided as a hosted multi-tenant environment letting its customers to solve their particular analytical use case or many analytical use cases for thousands of their customers all using the same platform and tools while abstracting them away from the technological details of the big data stack.
AWS Big Data Demystified #1.2 | Big Data architecture lessons learned Omid Vahdaty
A while ago I entered the challenging world of Big Data. As an engineer, at first, I was not so impressed with this field. As time went by, I realised more and more, The technological challenges in this area are too great to master by one person. Just look at the picture in this articles, it only covers a small fraction of the technologies in the Big Data industry…
Consequently, I created a meetup detailing all the challenges of Big Data, especially in the world of cloud. I am using AWS & GCP and Data Center infrastructure to answer the basic questions of anyone starting their way in the big data world.
how to transform data (TXT, CSV, TSV, JSON) into Parquet, ORC,AVRO which technology should we use to model the data ? EMR? Athena? Redshift? Spectrum? Glue? Spark? SparkSQL? GCS? Big Query? Data flow? Data Lab? tensor flow? how to handle streaming? how to manage costs? Performance tips? Security tip? Cloud best practices tips?
In this meetup we shall present lecturers working on several cloud vendors, various big data platforms such hadoop, Data warehourses , startups working on big data products. basically - if it is related to big data - this is THE meetup.
Some of our online materials (mixed content from several cloud vendor):
Website:
https://big-data-demystified.ninja (under construction)
Meetups:
https://www.meetup.com/Big-Data-Demystified
https://www.meetup.com/AWS-Big-Data-Demystified/
You tube channels:
https://www.youtube.com/channel/UCMSdNB0fGmX5dXI7S7Y_LFA?view_as=subscriber
https://www.youtube.com/channel/UCzeGqhZIWU-hIDczWa8GtgQ?view_as=subscriber
Audience:
Data Engineers
Data Science
DevOps Engineers
Big Data Architects
Solution Architects
CTO
VP R&D
The talk is on How to become a data scientist. This was at 2ns Annual event of Pune Developer's Community. It focuses on Skill Set required to become data scientist. And also based on who you are what you can be.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2lGNybu.
Stefan Krawczyk discusses how his team at StitchFix use the cloud to enable over 80 data scientists to be productive. He also talks about prototyping ideas, algorithms and analyses, how they set up & keep schemas in sync between Hive, Presto, Redshift & Spark and make access easy for their data scientists, etc. Filmed at qconsf.com..
Stefan Krawczyk is Algo Dev Platform Lead at StitchFix, where he’s leading development of the algorithm development platform. He spent formative years at Stanford, LinkedIn, Nextdoor & Idibon, working on everything from growth engineering, product engineering, data engineering, to recommendation systems, NLP, data science and business intelligence.
What is a data platform? Why do we need one? And how to build one in the cloud? This talk covers the essential engineering facets of a data platform: flows, persistence, access, standardization and data processing. How these facets combine into a unified platform and how and what cloud technologies as managed services and serverless help/challenge us to build it into a powerful business tool.
These are slides from a presentation from a "code naturally" meetup we held on 30/4 2018.
If we could only predict the future of the software industry, we could make better investments and decisions. We could waste less resources on technology and processes we know will not last, or at least be conscious in our decisions to choose solutions with a limited life time. It turns out that for data engineering, we can predict the future, because it has already happened. Not in our workplace, but at a few leading companies that are blazing ahead. It has also already happened in the neighbouring field of software engineering, which is two decades ahead of data engineering regarding process maturity. In this presentation, we will glimpse into the future of data engineering. Data engineering has gone from legacy data warehouses with stored procedures, to big data with Hadoop and data lakes, on to a new form of modern data warehouses and low code tools aka "the modern data stack". Where does it go from here? We will look at the points where data leaders differ from the crowd and combine with observations on how software engineering has evolved, to see that it points towards a new, more industrialised form of data engineering - "data factory engineering".
Beyond Wordcount with spark datasets (and scalaing) - Nide PDX Jan 2018Holden Karau
Apache Spark is one of the most popular big data systems, but once the shiny finish starts to wear off you can find yourself wondering if you've accidentally deployed a Ford Pinto into production. This talk will look at the challenges that come with scaling Spark jobs. Also, the talk will explore Spark's new(ish) Dataset/DataFrame API, as well as how it’s evolving in Spark 2.3 with improved Python support.
If you're already a Spark user, come to find out why it’s not all your fault. If you aren't already a Spark user, come to find out how to save yourself from some of the pitfalls once you move beyond the example code.
Check out Holden's newest book, High Performance Spark, for more information!
From https://niketechtalksjan2018.splashthat.com/
Similar to Dirty data? Clean it up! - Datapalooza Denver 2016 (20)
My talk from Database Camp 2016 at the United Nations. I focus on how we can bridge the gap between OLTP and OLAP workloads and discuss a very promising new technology called Apache Kudu.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Dirty data? Clean it up! - Datapalooza Denver 2016
1. Dirty Data? Clean it up!
Or, how to do data science in the real world.
Dan Lynn
CEO, AgilData
@danklynn
dan@agildata.com
Patrick Russell
Director, Data Science, Craftsy
@patrickrm101
patrick@craftsy.com
15. Data Cleansing
● Dates & Times
● Numbers & Strings
● Addresses
● Clickstream Data
● Handling missing data
● Tidy Data
16. Dates & Times
● Timestamps can mean different things
○ ingested_date, event_timestamp
● Clocks can’t be trusted
○ Server time: which server? Is it synchronized?
○ Client time? Is there a synchronizing time scheme?
● Timezones
○ What tz is your own data in?
○ Your email provider? Your adwords account? Your Google Analytics?
17. Numbers & Strings
● Use the right types for your numbers (int, bigint, float, numeric
etc)
● Murphy’s Law of text inputs: If a user can put something in a text
field, anything and everything will happen.
● Watch out for floating point precision mistakes
18. Addresses
● Parsing / validation is not something you want to do yourself
○ USPS has validation and zip lookup for US addresses: https://www.usps.
com/business/web-tools-apis/documentation-updates.htm
● Remember zip codes are strings. And the rest of the world does not
use U.S. zips.
● IP geolocation: Get lat/long, state, city, postal & ISP, from visitor
IPs
○ https://www.maxmind.com/en/geoip2-city
○ This is ALWAYS approximate
● If working with GIS, recommend http://postgis.net/
○ Vanilla postgres also has earthdistance for great circle distance
19. Clickstream Data
● User agent => Device: Don’t do this yourself (we use WURFL and Google
Analytics)
● Query strings follow the rules of text. Everything will show up
○ They might be truncated
○ URL encoding might be missing characters (%2 instead of %20)
○ Use a library to parse params (ie Python ships with urlparse.parse_qs)
● If your system creates sessions (tomcat, Google Analytics), don’t be
afraid to create your own sessions on top of the pageview data
○ You’ll cross channel and cross device behavior this way
21. Missing / empty data
● Easy to overlook but important
● What does missing data in the context of your analysis mean?
○ Not collected (why not?)
○ Error state
○ N/A or undefined
○ Especially for histograms, missing data lead to very poor conclusions.
● Does your data use sentinel values? (ie -9999 or “null”)
○ df[‘nps_score’].replace(-9999, np.nan)
● Imputation
● Storage
22. Tidy Data
● Conceptual framework for structuring data for analysis and fitting
○ Each variable forms a column
○ Each observation is a row
○ Each type of observational unit forms a table
● Pretty much normal form from relational databases for stats
● Tidy can be different depending on the question asked
● R (dplyr, tidyr) and Python (pandas) have functions for making your
long data wide & wide data long (stack, unstack, melt, pivot)
● Paper: http://vita.had.co.nz/papers/tidy-data.pdf
● Python tutorial: http://tomaugspurger.github.io/modern-5-tidy.html
23. Tidy Data
● Example might be market place transaction data with 1 row per
transaction
● You might want to do analysis on participants, 1 row per participant
24. Hey, that’s a great model. How can we build it
into our decision-making process?
— Marketing
26. ● Doing an analysis once rarely delivers lasting value.
● The business needs continuous insight, so you need to get this stuff
into production.
○ Hosting
○ ETL
○ Pipelines
Operationalizing Data Science
27. Hosting
● Delivering continuous analyses requires operational infrastructure
○ Database(s)
○ Visualization tools (e.g. Chartio, Arcadia Data, Tableau, Looker, Qlik, etc..)
○ REST services / microservices
● These all have uptime requirements. You need to involve your (dev)ops
team earlier rather than later.
● Microservices / REST endpoints have architectural implications
● Visualization tools
○ Local (e.g. Jupyter, Zeppelin)
○ On-premise (Arcadia Data, Tableau, Qlik)
○ Hosted (Chartio)
● Visualization tools often require a SQL interface, thus….
28. ETL - Extract, Transform, Load
● Often used to herd data into some kind of data warehouse (e.g. RDBMS
+ star schema, Hadoop w/ unstructured data, etc..)
● Not just for data warehousing
● Not just for modeling
● No general solution
● Tooling
○ Apache Spark, Apache Sqoop
○ Commercial Tools: Informatica, Vertica, SQL Server, DataVirtuality etc…
● And then there is Apache Kafka…and the “NoETL” movement
○ Book: “I <3 Logs” - by Jay kreps
○ Replay history from the beginning of time as needed
29. ETL - Extract, Transform, Load - Example
● Not just for production runs
○ For example, Patrick does a lot of time-to-event analysis on email opens,
transactions, visits.
■ Survival functions, etc...
○ Setup ETL that builds tables With the right shape to put right into models
30. Pipelines
● From data to model output
● Define dependencies and define DAG for the work
○ Steps defined by assigning input as output of prior steps
○ Luigi (http://luigi.readthedocs.io/en/stable/index.html)
○ Drake (https://github.com/Factual/drake)
○ Scikit learn has its own Pipeline
■ That can be part of your bigger pipeline
● Scheduling can be trickier than you think
○ Resource contention
○ Loose dependencies
○ Cron is fine but Jenkins works really well for this!
● Don’t be afraid to create and teardown full environments as steps
○ For example, spin up and configure an EMR cluster, do stuff, tear it down*
* make your VP of Infrastructure less miserable
31. Pipelines - Luigi
● Written in Python. Steps implemented by subclassing Task
● Visualize your DAG
● Supports data in relational DBs, Redshift, HDFS, S3, file system
● Flexible and extensible
● Can parallelize jobs
● Workflow runs by executing last step scheduling all dependencies
33. Pipelines - Drake
● JVM (written in Clojure)
● Like a Makefile but for data work
● Supports commands in Shell, Python, Ruby, Clojure
34. Pipelines - More Tools
● Oozie
○ The default job orchestration engine for Hadoop. Can chain together multiple jobs
to form a complete DAG.
○ Open source
● Kettle
○ Old-school, but still relevant.
○ Visual pipeline designer. Execution engine
○ Open source
● Informatica
○ Visual pipeline designer, mature toolset
○ Commercial
● Datavirtuality
○ Treats all your stores (including Google Analytics) like schemas in a single db
○ Great for microservice architectures
○ Commercial
36. References
● I Heart Logs
○ http://www.amazon.com/Heart-Logs-Stream-Processing-Integration/dp/1491909382
● Tidy Data
○ http://vita.had.co.nz/papers/tidy-data.pdf
37. Additional Tools
● Scientific python stack (ipython, numpy, scipy, pandas, matplotlib…)
● Hadleyverse for R (dplyr, ggplot, tidyr, lubridate…)
● csvkit: command line tools (csvcut, csvgrep, csvjoin...) for CSV data
● jq: fast command line tool for working with json (ie pipe cURL to jq)
● psql (if you use postgresql or Redshift)