Databricks for Data Engineers

With GDPR enforcement rapidly approaching on May 25, many companies are still trying to figure out how to comply with one of the regulation’s biggest pain points - data subject requests (DSRs). Under GDPR, data subjects (individuals) in the EU have the right to request information on what personal data is collected, how it is being used, and to have that data changed or erased.

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:
• The GDPR requirements of data subject requests
• The challenges big data and data lakes create for organizations
• 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
• Best practices for GDPR data governance
• Live demo on how to easily fulfill data requests with Databricks

Sandy is going to highlight some key aspects of the new Spark-as-a-Service offering in Azure, from Databricks. Leveraging the power of Databricks notebooks to showcase loading and cleaning data in SQL and Scala, exploration and all the way through to having a model into production.

Azure Databricks in an Apache Spark™ based platform, providing the scale, collaborative platform, and integration with your Azure environment that makes it the best place to run your ML and AI workloads on Azure. This webinar will include an in-depth demo of key AI and ML use cases.

With 170+ global networks, Viacom is focused on providing an amazing audience experience to its billions of viewers around the world. Core to this strategy is leveraging big data and advanced analytics to offer the right content to the right audience and deliver it flawlessly on any device. To make this possible, Viacom set-out to build a real-time, scalable data analytics platform on Apache Spark™.

Join this webinar to learn how Viacom overcame the complexities of Spark with Databricks and AWS to build an end-to-end scalable self-service insights platform that delivers on a wide range of analytics use cases.

This webinar will cover:
- The challenges Viacom faced building a scalable, real-time data insights and AI platform
- How they overcame these challenges with Spark, AWS and Databricks
- How they leverage a unified analytics platform for data pipelines, analytics and machine learning to reduce video start delays and improve content delivery with stream analytics at scale
- 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

Learn the basics of Apache Spark™ on Azure Databricks. Designed by Databricks, in collaboration with Microsoft, Azure Databricks combines the best of Databricks and Azure to help customers accelerate innovation with one-click set up, streamlined workflows and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts.

This webinar will cover the following topics:

· RDDs, DataFrames, Datasets, and other fundamentals of Apache Spark.
· How to quickly setup Azure Databricks, relieving you of DataOps duties.
· 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.

Building multiple ETL pipelines is very complex and time consuming, making it a very expensive endeavor. As the number of data sources and the volume of the data increases, the ETL time also increases, negatively impacting when an enterprise can derive value from the data.

Join Prakash Chockalingam, Product Manager and data engineering expert at Databricks, to learn how to avoid the common pitfalls of data engineering and how the Databricks Unified Analytics Platform can ensure performance and reliability at scale to lower total cost of ownership (TCO).

In this webinar, you will learn how Databricks can help to:
- Remove infrastructure configuration complexity to reduce DevOps efforts
- Optimize your ETL data pipelines for performance without compromising reliability
- Unify data engineering and data science to accelerate innovation for the business.

Data scientists and data engineers need a secure and scalable platform to collaborate on analytics. Register for this webinar and see how Azure Databricks provides a platform that enables teams to accelerate innovation, providing:

- A collaborative workspace to experiment with models and datasets, and then put jobs into action instantly.
- An automated infrastructure that enables you to autoscale compute and storage independently.

The live demo portion of the webinar will show how Azure Databricks can bring in streaming data, run it in a machine learning model, and then output the results to PowerBI for visualization.

The upcoming Spark 2.3 release marks a big step forward in speed, unification, and API support.

Reynold Xin and Jules Damji from Databricks will walk through how you can benefit from the upcoming improvements:

- New DataSource APIs that enable developers to more easily read and write data for Continuous Processing in Structured Streaming.
- PySpark support for vectorization, giving Python developers the ability to run native Python code fast.
- Improved performance by taking advantage of NVMe SSDs.
- Native Kubernetes support, marrying the best of container orchestration and distributed data processing.

Enterprise data science teams are driving big innovations in machine learning, but this has put them under increased pressure to deliver more models, more frequently, and more rapidly.

In this webinar, Forrester VP & Principal Analyst, Mike Gualtieri, will share data on the top trends in machine learning and lay out what data science teams need to do in order to maximize their output.

Chris Robison, Head of Data Science at Overstock.com and Craig Kelly, Group Product Manager at Overstock.com, will showcase how they utilized big data and machine learning to

-Create a one-to-one personalized shopping experience.
-Decrease cost of moving models to production by nearly 50%.
-Stand up new models 5x faster than before.

iPass 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.

Analyzing the state of the world’s wifi in real time is a daunting task fraught with unpredictable challenges that can impact performance, reliability, and security. Join this webinar to learn why iPass moved from an on-premises Hadoop system to Databricks in the cloud and how they are able to deliver ground-breaking results with a small and nimble team.

With Databricks, iPass can now focus on scalable business logic and not building infrastructure. This new found freedom has allowed their team to:
-monitor the performance of millions of wifi hotspots around world.
-leverage machine learning and real-time analytics to understand the health of access points.
-make recommendations to customers on the best access point to use to ensure optimal performance.

Continuous 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.

Current data management architectures are a complex combination of siloed, single-purpose tools. There are data lakes for low cost storage, but are difficult to use for data discovery, data warehouses that are reliable and optimized for fast queries, but come at a cost when having to scale, and various streaming and batch systems to shuffle data between them, often times resulting in data integrity issues.

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?

Join this webinar to learn how Databricks Delta — a new unified data management system — takes advantage of the the scale of a data lake, the reliability and performance of a data warehouse, and the low-latency updates of a streaming system, all in a unified and fully managed fashion.

This webinar will cover:
-How the need to process batch and streaming data creates challenges for enterprises with complex data architectures.
-How Databricks Delta takes the best of data warehouses, data lakes and streaming systems to provide a highly scalable, performant, and reliable data management system.
-A live demonstration of Databricks Delta to showcase how easy it is to cost-efficiently scale without impacting query performance.

Machine 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.

Join us for this webinar and hear from John Huang, engineering and data analytics lead at Handshake, as he shares how he quickly and cost effectively scaled a small engineering team to build an machine-learning powered recommendation engine that profiles users and behaviors to present relevant next steps. In this webinar you will learn how to:

-Simplify and accelerate data engineering processes including data ingest and ETL
-Incorporate machine learning into your production application without an army of data scientists
-Choose an analytics engine that will enable key analytics such as attribution, step analysis, and linear regression
-Embed visualizations into your application that drive stickiness

A key obstacle for doing data engineering at scale is having a robust distributed infrastructure on which frameworks like Apache Spark can run efficiently. On top of building the infrastructure, having proper automatic functioning of the infrastructure is another critical piece for running production workloads.

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.

CardinalCommerce 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.

With Databricks, CardinalCommerce simplified data engineering to improve the performance of their ETL pipeline by 200% while reducing operational costs significantly via automation, seamless integration with key technologies, and improved process efficiencies.

Join this webinar to learn how CardinalCommerce was able to:
-Simplify access to data across the organization
-Accelerate data processing by 200%
-Reduce EC2 costs through faster performance and automated infrastructure
-Visualize performance metrics to customers and stakeholders

Performance 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!

Data science and machine learning tools traditionally focus on training models. When companies begin to employ machine learning in actual production workflows, they encounter new sources of friction such as sharing models across teams, deploying identical models on different systems, and maintaining featurization logic. In this webinar, we discuss how Databricks provides a smooth path for productionizing Apache Spark MLlib models and featurization pipelines.

Databricks Model Scoring provides a simple API for exporting MLlib models and pipelines. These exported models can be deployed in many production settings, including:

* External real-time low-latency prediction serving systems, without Spark dependencies,
* Apache Spark Structured Streaming jobs, and
* Apache Spark batch jobs.

In this webinar, we overview our solution’s functionality, describe its architecture, and demonstrate how to use it to deploy MLlib models to production.

Deep Learning has shown a tremendous success, yet it often requires a lot of effort to leverage its power. Existing Deep Learning frameworks require writing a lot of code to work with a model, let alone in a distributed manner.

This webinar is the first of a series in which we survey the state of Deep Learning at scale, and where we introduce the Deep Learning Pipelines, a new open-source package for Apache Spark. This package simplifies Deep Learning in three major ways:

1. It has a simple API that integrates well with enterprise Machine Learning pipelines.
2. It automatically scales out common Deep Learning patterns, thanks to Spark.
3. It enables exposing Deep Learning models through the familiar Spark APIs, such as MLlib and Spark SQL.

In this webinar, we will look at a complex problem of image classification, using Deep Learning and Spark. Using Deep Learning Pipelines, we will show:

* how to build deep learning models in a few lines of code;
* how to scale common tasks like transfer learning and prediction; and
* how to publish models in Spark SQL.

Whether you’re processing IoT data from millions of sensors or building a recommendation engine to provide a more engaging customer experience, the ability to derive actionable insights from massive volumes of diverse data is critical to success. MediaMath, a leading adtech company, relies on Apache Spark to process billions of data points ranging from ads, user cookies, impressions, clicks, and more — translating to several terabytes of data per day. To support the needs of the data science teams, data engineering must build data pipelines for both ETL and feature engineering that are scalable, performant, and reliable.

Join this webinar to learn how MediaMath leverages Databricks to simplify mission-critical data engineering tasks that surface data directly to clients and drive actionable business outcomes. This webinar will cover:

- Transforming TBs of data with RDDs and PySpark responsibly
- Using the JDBC connector to write results to production databases seamlessly
- Comparisons with a similar approach using Hive