Apache Spark 2.0: Faster, Easier, and Smarter
In this webcast, Reynold Xin from Databricks will be speaking about Apache Spark's new 2.0 major release.
The major themes for Spark 2.0 are:
- Unified APIs: Emphasis on building up higher level APIs including the merging of DataFrame and Dataset APIs
- Structured Streaming: Simplify streaming by building continuous applications on top of DataFrames allow us to unify streaming, interactive, and batch queries.
- Tungsten Phase 2: Speed up Apache Spark by 10X
The major themes for Spark 2.0 are:
- Unified APIs: Emphasis on building up higher level APIs including the merging of DataFrame and Dataset APIs
- Structured Streaming: Simplify streaming by building continuous applications on top of DataFrames allow us to unify streaming, interactive, and batch queries.
- Tungsten Phase 2: Speed up Apache Spark by 10X
Recorded
May 5 2016
61 mins
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Presented by
Reynold Xin
Reynold Xin
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Introducing MLflow: Infrastructure for a Complete Machine Learning Lifecycle Recorded: Aug 30 2018 54 mins
Matei Zaharia, Co-Founder and Chief Technologist at Databricks, Denny Lee, Technical Product Marketing Manager at DatabricksML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In our webinar, we will present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
We will show how to:
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Get started now at https://www.mlflow.org/ -
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For many organizations that rely on data lakes to store their big data, sifting through millions of files to locate and modify records for a DSR is at minimum a massive effort. And trying to do this within prescribed timelines is near impossible.
Fortunately there’s a path forward. Through an optimized approach to data management, Databricks powered by Apache Spark™ makes it easy to quickly find, edit and erase data submerged deep within your data lake without disrupting your data pipelines.
Join this webinar to learn:
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• How Databricks Delta, a powerful new offering within the Databricks Unified Analytics Platform improves data lake management and makes it possible to quickly find and surgically remove or modify individual records
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• Live demo on how to easily fulfill data requests with Databricks -
Collaboration to Production with Apache Spark on Azure Databricks Recorded: Apr 27 2018 54 mins
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Apache Spark™ for Machine Learning and AI Recorded: Apr 26 2018 61 mins
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How Viacom Revolutionized Audience Experiences with Real-Time Analytics at Scale Recorded: Apr 25 2018 59 mins
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- What it takes to create a data driven culture with self-service analytics that meet the needs of business users, data analysts and data scientists -
Getting Started with Apache Spark™ on Azure Databricks Recorded: Mar 27 2018 60 mins
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· How to use the Databricks interactive notebooks, which provide a collaborative space for your entire analytics team, and how you can schedule notebooks, immediately putting your work into production. -
Fast and Reliable ETL Pipelines with Databricks Recorded: Mar 7 2018 57 mins
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In this webinar, you will learn how Databricks can help to:
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- Unify data engineering and data science to accelerate innovation for the business. -
Azure Databricks: Accelerating Innovation with Microsoft Azure and Databricks Recorded: Feb 15 2018 52 mins
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What's New in the Upcoming Apache Spark 2.3 Release? Recorded: Feb 8 2018 49 mins
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- Improved performance by taking advantage of NVMe SSDs.
- Native Kubernetes support, marrying the best of container orchestration and distributed data processing. -
Ten Must-Haves to Deploy Machine Learning and AI in the Enterprise Recorded: Jan 25 2018 61 mins
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-Decrease cost of moving models to production by nearly 50%.
-Stand up new models 5x faster than before. -
How Databricks helps iPass optimize for performance and availability Recorded: Jan 10 2018 60 mins
Tomasz Magdanski, Director of Big Data and Analytics at iPassiPass is the world’s largest wifi network serving over 160 network providers with nearly 60+ million hotspots in airports, hotels, airplanes, and public spaces in 120 countries across the globe.
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-make recommendations to customers on the best access point to use to ensure optimal performance. -
Continuous Integration & Continuous Delivery with Databricks Recorded: Dec 7 2017 45 mins
Prakash Chockalingam, Product Manager at DatabricksContinuous integration and continuous delivery (CI/CD) enables an organization to rapidly iterate on software changes while maintaining stability, performance, and security. Many organizations have adopted various tools to follow the best practices around CI/CD to improve developer productivity, code quality, and software delivery. However, following the best practices of CI/CD is still challenging for many big data teams.
This webinar will highlight:
*Key challenges in building a data pipeline for CI/CD.
*Key integration points in a data pipeline's CI/CD cycle.
*How Databricks facilitates iterative development, continuous integration and build. -
Unified Data Management: The Best of Data Lakes, Data Warehouses and Streaming Recorded: Nov 16 2017 61 mins
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Businesses have to create a patchwork of different tools, skillsets, and expertise just to solve one fundamental problem: How can I make data-driven decisions faster?
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This webinar will cover:
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-A live demonstration of Databricks Delta to showcase how easy it is to cost-efficiently scale without impacting query performance. -
5 Keys to Build Machine Learning and Visualization Into Your Application Recorded: Nov 8 2017 51 mins
Databricks, Handshake, and LookerMachine learning has unlocked new possibilities that deliver significant business value. However most companies don’t have the resources to either build and maintain the supporting infrastructure or apply data science to build a smarter solution.
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-Embed visualizations into your application that drive stickiness -
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Join this webinar to learn how Databricks’ Unified Analytics Platform can help simplify your data engineering problems by configuring your distributed infrastructure to be in autopilot mode. Learn how:
-Databricks’ automated infrastructure will allow you to autoscale compute and storage independently.
-To significantly reduce cloud costs through cutting edge cluster management features.
-To control certain features in the cluster management and balance between ease of use and manual control. -
How CardinalCommerce Significantly Improved Data Pipeline Speeds by 200% Recorded: Sep 21 2017 61 mins
Christopher Baird from CardinalCommerceCardinalCommerce was acquired by Visa earlier this year for its critical role in payments authentication. Through predictive analytics and machine learning, Cardinal measures performance and behavior of the entire authentication process across checkout, issuing and ecosystem partners to recommend actions, reduce fraud and drive frictionless digital commerce.
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Join this webinar to learn how CardinalCommerce was able to:
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Performance Benchmarking Big Data Platforms in the Cloud Recorded: Aug 22 2017 47 mins
Reynold Xin, Co-founder and Chief Architect at DatabricksPerformance is often a key factor in choosing big data platforms. Over the past few years, Apache Spark has seen rapid adoption by enterprises, making it the de facto data processing engine for its performance and ease of use.
Since starting the Spark project, our team at Databricks has been focusing on accelerating innovation by building the most performant and optimized Unified Analytics Platform for the cloud. Join Reynold Xin, Co-founder and Chief Architect of Databricks as he discusses the results of our benchmark (using TPC-DS industry standard requirements) comparing the Databricks Runtime (which includes Apache Spark and our DBIO accelerator module) with vanilla open source Spark in the cloud and how these performance gains can have a meaningful impact on your TCO for managing Spark.
This webinar covers:
Differences between open source Spark and Databricks Runtime.
Details on the benchmark including hardware configuration, dataset, etc.
Summary of the benchmark results which reveal performance gains by up to 5x over open source Spark and other big data engines.
A live demo comparing processing speeds of Databricks Runtime vs. open source Spark.
Special Announcement: We will also announce an experimental feature as part of the webinar that aims at drastically speeding up your workloads even more. Be the first to see this feature in action. Register today!
Making Big Data Simple
Databricks’ mission is to accelerate innovation for its customers by unifying Data Science, Engineering and Business. Founded by the team who created Apache Spark™, Databricks provides a Unified Analytics Platform for data science teams to collaborate with data engineering and lines of business to build data products. Users achieve faster time-to-value with Databricks by creating analytic workflows that go from ETL and interactive exploration to production. The company also makes it easier for its users to focus on their data by providing a fully managed, scalable, and secure cloud infrastructure that reduces operational complexity and total cost of ownership.
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- Title: Apache Spark 2.0: Faster, Easier, and Smarter
- Live at: May 5 2016 5:00 pm
- Presented by: Reynold Xin
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