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Modern data analytics often involves large datasets that do not fit into memory or are too slow to process with traditional tools. Apache Spark is one of the most widely used frameworks for scalable data processing and analytics.

This session gives a clear and practical introduction to data analytics using Apache Spark and Python. It focuses on understanding how Spark works and how to use it to analyse large datasets efficiently.

Who is this for?

Students, developers, and anyone who works with data and wants to analyse large datasets using Python. This session is useful if you have outgrown Pandas, work with big CSV or log files, or want to learn how modern data analytics systems handle scale.

Who is leading the session?

The session is led by Dr. Stelios Sotiriadis, CEO of Warestack and Associate Professor and MSc Programme Director at Birkbeck, University of London. He works in big data systems, distributed computing, cloud platforms, and Python-based data analytics. He holds a PhD from the University of Derby, completed a postdoctoral fellowship at the University of Toronto, and has worked with Huawei, IBM, Autodesk, and several startups. Since 2018, he has been teaching at Birkbeck and founded Warestack in 2021.

What we will cover

This is a hands-on introduction with practical examples and short exercises. Topics include loading data into Spark, understanding DataFrames, basic transformations and actions, filtering and aggregations, grouping and joins, using Spark SQL, and understanding when and why Spark is better than Pandas.

Requirements

A laptop with Python installed (Windows, macOS, or Linux), Visual Studio Code, and Python pip. Spark will be provided via local setup or lab environment. Lab computers can be used if needed.

Format

A 1.5-hour live session with short explanations, live coding, and guided exercises. The session runs in person, with streaming available for remote participants.

Prerequisites

Basic to intermediate Python knowledge, including functions, loops, and basic data structures. Prior experience with Pandas is helpful but not required.

This book serves as the ultimate preparation for aspiring Databricks Certified Associate Developers specializing in Apache Spark. Deep dive into Spark's components, its applications, and exam techniques to achieve certification and expand your practical skills in big data processing and real-time analytics using Python. What this Book will help me do Deeply understand Apache Spark's core architecture for building big data applications. Write optimized SQL queries and leverage Spark DataFrame API for efficient data manipulation. Apply advanced Spark functions, including UDFs, to solve complex data engineering tasks. Use Spark Streaming capabilities to implement real-time and near-real-time processing solutions. Get hands-on preparation for the certification exam with mock tests and practice questions. Author(s) Saba Shah is a seasoned data engineer with extensive experience working at Databricks and leading data science teams. With her in-depth knowledge of big data applications and Spark, she delivers clear, actionable insights in this book. Her approach emphasizes practical learning and real-world applications. Who is it for? This book is ideal for data professionals such as engineers and analysts aiming to achieve Databricks certification. It is particularly helpful for individuals with moderate Python proficiency who are keen to understand Spark from scratch. If you're transitioning into big data roles, this guide prepares you comprehensively.

Please join us on 22nd May, 2024 to listen to the topic: Databricks Workflows CICD and Automated Testing

What \~ Toronto Data Professionals Community (Virtual) When \~ Wednesday, 22nd May, 2024

Agenda:

• 6:00 PM Networking and Introduction
• 6:15 PM Topic: Databricks Workflows CICD and Automated Testing with Dustin Vannoy
• 7:30 PM End

Where: Online via Microsoft team

Session Details: Databricks Workflows (also known as Jobs) are a great choice for automating data pipelines. Once the code is ready comes the important step of promoting beyond your dev environment. Continuous Integration / Continuous Deployment (CI/CD) involves versioning, testing, and deploying your data processing jobs. Databricks provides tools that allow us to follow these DevOps best practices, but how do we put these together to ensure quality and manage workflow promotion across isolated environments? Join this session to learn some of the most common ways teams leverage Databricks to version, test, and deploy their automated data pipelines. In this session we cover some basic CI/CD concepts and the options within Databricks. Then we walk through an example of merging, testing, and deploying a workflow change.

Speaker Bio: Dustin Vannoy is a Data Engineering Consultant experienced in solving business problems with analytics and big data solutions. He is passionate about all aspects of data engineering, especially building data platforms and streaming data pipelines. He currently focuses on building data platforms and pipelines in Apache Spark / Databricks, Kafka, Python, and Scala. He is co-founder of the Data Engineering San Diego meetup and encourages others to grow their data skills by making tutorials, mentoring others, and speaking at events.

In recent years, latest privacy laws & regulations bring a fundamental shift in the protection of data and privacy, placing new challenges to data applications. To resolve these privacy & security challenges in big data ecosystem without impacting existing applications, several hardware TEE (Trusted Execution Environment) solutions have been proposed for Apache Spark, e.g., PySpark with Scone and Opaque etc. However, to the best of our knowledge, none of them provide full protection to data pipelines in Spark applications. An adversary may still get sensitive information from unprotected components and stages. Furthermore, some of them greatly narrowed supported applications, e.g., only support SparkSQL. In this presentation, we will present a new PPMLA (privacy preserving machine learning and analytics) solution built on top of Apache Spark, BigDL, Occlum and Intel SGX. It ensures all spark components and pipelines are fully protected by Intel SGX, and existing Spark applications written in Scala, Java or Python can be migrated into our platform without any code change. We will demonstrate how to build distributed end-to-end SparkML/SparkSQL workloads with our solution on untrusted cloud environment and share real-world use cases for PPMLA.

Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/data... Instagram: https://www.instagram.com/databricksinc/

Being a pioneer for the past 25 years, SONY PlayStation has played a vital role in the Interactive Gaming Industry. Over 100+ million monthly active users, 100+ million PS-4 console sales along with thousands of game development partners across the globe, big-data problem is quite inevitable. This presentation talks about how we scaled Airflow horizontally which has helped us building a stable, scalable and optimal data processing infrastructure powered by Apache Spark, AWS ECS, EC2 and Docker. Due to the demand for processing large volumes of data and also to meet the growing Organization’s data analytics and usage demands, the data team at PlayStation took an initiative to build an open source big data processing infrastructure where Apache Spark in Python as the core ETL engine. Apache Airflow is the core workflow management tool for the entire eco system. We started with an Airflow application running on a single AWS EC2 instance to support parallelism of 16 with 1 scheduler and 1 worker and eventually scaled it to a bigger scheduler along with 4 workers to support a parallelism of 96, DAG concurrency of 96 and a worker task concurrency of 24. Containerized all the services on AWS ECS which gave us an ability to scale Airflow horizontally.

"Learning Spark SQL" takes you from data exploration to designing scalable applications with Apache Spark SQL. Through hands-on examples, you will comprehend real-world use cases and gain practical skills crucial for working with Spark SQL APIs, data frames, streaming data, and optimizing Spark applications. What this Book will help me do Understand the principles of Spark SQL and its APIs for building scalable distributed applications. Gain hands-on experience performing data wrangling and visualization using Spark SQL and real-world datasets. Learn how to design and optimize applications for performance and scalability with Spark SQL. Develop the skills to integrate Spark SQL with other frameworks like Apache Kafka for streaming analytics. Master the techniques required to architect machine learning and deep learning solutions using Spark SQL. Author(s) None Sarkar is an experienced technologist and trainer specializing in big data, streaming analytics, and scalable architectures using Apache Spark. With years of practical experience in implementing Spark solutions, Sarkar draws from real-world projects to provide readers with valuable insights. Sarkar's approachable and detailed writing style ensures readers grasp both the theory and the practice of Spark SQL. Who is it for? This book is ideal for software developers, data engineers, and architects aspiring to harness Apache Spark for robust, scalable applications. It suits readers with some SQL querying experience and a basic knowledge of programming in languages like Scala, Java, or Python. Whether you're a Spark newcomer or advancing your capabilities in scalable data processing, this resource will accelerate your learning journey.

Summary

There is a vast constellation of tools and platforms for processing and analyzing your data. In this episode Matthew Rocklin talks about how Dask fills the gap between a task oriented workflow tool and an in memory processing framework, and how it brings the power of Python to bear on the problem of big data.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Matthew Rocklin about Dask and the Blaze ecosystem.

Interview with Matthew Rocklin

Introduction How did you get involved in the area of data engineering? Dask began its life as part of the Blaze project. Can you start by describing what Dask is and how it originated? There are a vast number of tools in the field of data analytics. What are some of the specific use cases that Dask was built for that weren’t able to be solved by the existing options? One of the compelling features of Dask is the fact that it is a Python library that allows for distributed computation at a scale that has largely been the exclusive domain of tools in the Hadoop ecosystem. Why do you think that the JVM has been the reigning platform in the data analytics space for so long? Do you consider Dask, along with the larger Blaze ecosystem, to be a competitor to the Hadoop ecosystem, either now or in the future? Are you seeing many Hadoop or Spark solutions being migrated to Dask? If so, what are the common reasons? There is a strong focus for using Dask as a tool for interactive exploration of data. How does it compare to something like Apache Drill? For anyone looking to integrate Dask into an existing code base that is already using NumPy or Pandas, what does that process look like? How do the task graph capabilities compare to something like Airflow or Luigi? Looking through the documentation for the graph specification in Dask, it appears that there is the potential to introduce cycles or other bugs into a large or complex task chain. Is there any built-in tooling to check for that before submitting the graph for execution? What are some of the most interesting or unexpected projects that you have seen Dask used for? What do you perceive as being the most relevant aspects of Dask for data engineering/data infrastructure practitioners, as compared to the end users of the systems that they support? What are some of the most significant problems that you have been faced with, and which still need to be overcome in the Dask project? I know that the work on Dask is largely performed under the umbrella of PyData and sponsored by Continuum Analytics. What are your thoughts on the financial landscape for open source data analytics and distributed computation frameworks as compared to the broader world of open source projects?

Keep in touch

@mrocklin on Twitter mrocklin on GitHub

Links

http://matthewrocklin.com/blog/work/2016/09/22/cluster-deployments?utm_source=rss&utm_medium=rss https://opendatascience.com/blog/dask-for-institutions/?utm_source=rss&utm_medium=rss Continuum Analytics 2sigma X-Array Tornado

Website Podcast Interview

Airflow Luigi Mesos Kubernetes Spark Dryad Yarn Read The Docs XData

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast