Disrupting Big Data with Apache Spark in the Cloud

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This document discusses the challenges of big data analytics and how Apache Spark and Databricks can help address them. It summarizes that: 1) There is a gap between the growth of data and ability to perform real-time analytics on that data due to challenges in managing infrastructure, empowering teams, and establishing production-ready applications. 2) Databricks provides a cloud-hosted platform that uses Apache Spark to allow for just-in-time processing of data across storage silos, with an integrated workspace for interactive exploration, machine learning, and production-ready workflows. 3) Databricks Enterprise Security provides an end-to-end security solution for Apache Spark to address challenges in securing file

Data & Analytics

Disrupting Big Data with
Apache Spark in the Cloud
Ali Ghodsi
Co-Founder and CEO

The Dawn of Advanced Analytics
2
WatsonSIRI/assistantsSelf-driving
cars
Not just sci-fi, important applications for businesses

Analytics Transforming Industries
3
Predictive analytics Anomaly Detection
Predict Product Revenue
Customer Assessment
Targeted Advertising
Fraud Detection
Risk Assessment
Equipment Failure
Data-Driven Real-time Analytics Applications

Today’s Data Reality
4
HADOOP
DATA LAKES
DATA HUBS
CLOUD
STORAGE
DATA
WAREHOUSE
S
Siloed, Fast-Growing Size, Cost

The Analytics Gap
5
IndustrialMediaPharma
HADOOP
DATA LAKES
DATA HUBS
CLOUD
STORAGE
DATA
WAREHOUSES
Siloed, Fast-Growing Size, Cost
Real-time Data-Driven Analytics Applications

Why is there a gap?
6
Real-time Data-Driven Analytics Applications
Manage Data
infrastructure
• Create, tune, monitor compute clusters.
• Securely access silos of disparate data sources.
• Enforce proper data governance.
•1
Empower teams to be
productive
• Securely share big data clusters among analysts.
• Interactively explore data and prototype ideas.
• Debug, troubleshoot, version-control big data applications.•
•
•
2
Establish Production-
Ready Applications
• Setup robust data pipelines for ETL/ELT.
• Productionize real-time applications with HA, FT.
• Build, serve, maintain advanced machine learning models.
•
3
Siloed, Fast-Growing Size, Cost

Databricks Cloud-Hosted Platform
7
• Separate compute & storage
• Integrate existing data stores
• Efficient cache on first access
Just-in-Time Data
Platform
1
Agile
• Workflow scheduler for ML,
streaming, SQL, ETL
• High availability, fault-
tolerant, performance-
optimized
Automated Apache
Spark Management
3
Production-Ready
• Interactive notebooks,
dashboards, reports
• Real-time exploration,
machine learning, graph use
cases
Integrated
Workspace
2
Democratize Big Data

HADOOP /
DATA LAKES
DATA
WAREHOUSE
S
YOUR
STORAGE
CLOUD
STORAGE
8
Databricks Just-in-Time Data Platform
INTEGRATED
WORKSPACEDASHBOARD
S
Reports
NOTEBOOKS
github, viz,
collaboration
BI TOOLS
JUST-IN-TIME
PROCESSING
POWERED
BY
APACHE
CLUSTERS: Auto-scaled, resilient, multi-tenant
DATA INTEGRATION: secure and fast data source
integrations
INTERFACES: REST APIs & BI tools
DATABRICKS SERVICES
+
YOUR CUSTOM SPARK
APPS
PRODUCTION JOBS
DATA LAKE
DATA HUB

The Challenge of Securing Analytics
9
End-to-end security a challenge for enterprises
Securing file
management
Secure table
management
Secure
cluster
management
Secure
job
workflows
Secure
dashboards,
report, notebook
management
Today there are piecemeal solutions, but no comprehensive
solution

Databricks Enterprise Security (DBES)
10
Holistic end-to-end security for Data Analytics
Tables Clusters Workflow
s
Notebooks,
Dashboards,
Reports
Files
• Role-based access control
• Auditing and governance
• Integrated identity-management
• Encryption on-disk and on-the-
wire
DBES provides
The First End-to-End Security Solution for Apache Spark

Enterprise use-cases
11
Preventing credit card fraud
Predict energy demand based on massive weather data
Predict player churn, predicting network outages
Natural language processing to extract author graph
Generating tailored programs based on big data

Try Apache Spark with Databricks
13
http://databricks.com/try
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This talk given at the Hadoop Summit in San Jose on June 28, 2016, analyzes a few major trends in Big Data analytics. These are a few takeaways from this talk: - Adopt Apache Beam for easier development and portability between Big Data Execution Engines. - Adopt stream analytics for faster time to insight, competitive advantages and operational efficiency. - Accelerate your Big Data applications with In-Memory open source tools. - Adopt Rapid Application Development of Big Data applications: APIs, Notebooks, GUIs, Microservices… - Have Machine Learning part of your strategy or passively watch your industry completely transformed! - How to advance your strategy for hybrid integration between cloud and on-premise deployments?

Analysis of Major Trends in Big Data Analytics

DataWorks Summit/Hadoop Summit

Analysis of Major Trends in Big Data Analytics

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Testing Big Data: Automated Testing of Hadoop with QuerySurge

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Are You Ready? Stepping Up To The Big Data Challenge In 2016 - Learn why Testing is pivotal to the success of your Big Data Strategy. According to a new report by analyst firm IDG, 70% of enterprises have either deployed or are planning to deploy big data projects and programs this year due to the increase in the amount of data they need to manage. The growing variety of new data sources is pushing organizations to look for streamlined ways to manage complexities and get the most out of their data-related investments. The companies that do this correctly are realizing the power of big data for business expansion and growth. Learn why testing your enterprise's data is pivotal for success with big data and Hadoop. Learn how to increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your data - all with one data testing tool.

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Simplifying Real-Time Architectures for IoT with Apache Kudu

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2022 Trends in Enterprise Analytics

DATAVERSITY

It is a fascinating, explosive time for enterprise analytics. It is from the position of analytics leadership that the mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics. The coming years will be full of big changes in enterprise analytics and Data Architecture. William will kick off the fourth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.

Microsoft Fabric Introduction

James Serra

Microsoft Fabric is the next version of Azure Data Factory, Azure Data Explorer, Azure Synapse Analytics, and Power BI. It brings all of these capabilities together into a single unified analytics platform that goes from the data lake to the business user in a SaaS-like environment. Therefore, the vision of Fabric is to be a one-stop shop for all the analytical needs for every enterprise and one platform for everyone from a citizen developer to a data engineer. Fabric will cover the complete spectrum of services including data movement, data lake, data engineering, data integration and data science, observational analytics, and business intelligence. With Fabric, there is no need to stitch together different services from multiple vendors. Instead, the customer enjoys end-to-end, highly integrated, single offering that is easy to understand, onboard, create and operate. This is a hugely important new product from Microsoft and I will simplify your understanding of it via a presentation and demo. Agenda: What is Microsoft Fabric? Workspaces and capacities OneLake Lakehouse Data Warehouse ADF Power BI / DirectLake Resources

Cloudian 451-hortonworks - webinar

Hortonworks

Join Cloudian, Hortonworks and 451 Research for a panel-style Q&A discussion about the latest trends and technology innovations in Big Data and Analytics. Matt Aslett, Data Platforms and Analytics Research Director at 451 Research, John Kreisa, Vice President of Strategic Marketing at Hortonworks, and Paul Turner, Chief Marketing Officer at Cloudian, will answer your toughest questions about data storage, data analytics, log data, sensor data and the Internet of Things. Bring your questions or just come and listen!

(real-time)²: Real-time data for real-time analytics with Kafka and ClickHouse

HostedbyConfluent

"Kafka and ClickHouse are two powerful and popular technologies that can work together to create a high-performance, real-time data processing and analytics pipeline. They complement each other pretty well, and their synergy can be leveraged in various use cases. In this talk, we'll explore this dynamic synergy between Kafka and ClickHouse with a live demonstration leveraging OpenSky data. We’ll use ClickPipes, the ClickHouse Cloud native Kafka integration, for building an end-to-end real-time data processing and analytics solution. We’ll also dive into the details of what you need to know if you want to build similar real-time analytics applications."

Big Data Analytics with Hadoop

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Finding Meaning in the Noise: Understanding Big Data with AWS Analytics

Amazon Web Services

Cortana Analytics Workshop: The "Big Data" of the Cortana Analytics Suite, Pa...

MSAdvAnalytics

Lance Olson. Cortana Analytics is a fully managed big data and advanced analytics suite that helps you transform your data into intelligent action. Come to this two-part session to learn how you can do "big data" processing and storage in Cortana Analytics. In the first part, we will provide an overview of the processing and storage services. We will then talk about the patterns and use cases which make up most big data solutions. In the second part, we will go hands-on, showing you how to get started today with writing batch/interactive queries, real-time stream processing, or NoSQL transactions all over the same repository of data. Crunch petabytes of data by scaling out your computation power to any sized cluster. Store any amount of unstructured data in its native format with no limits to file or account size. All of this can be done with no hardware to acquire or maintain and minimal time to setup giving you the value of "big data" within minutes. Go to https://channel9.msdn.com/ to find the recording of this session.

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2017 continues to be an exciting year for Apache Spark. I will talk about new updates in two major areas in the Spark community this year: stream processing with Structured Streaming, and deep learning with high-level libraries such as Deep Learning Pipelines and TensorFlowOnSpark. In both areas, the community is making powerful new functionality available in the same high-level APIs used in the rest of the Spark ecosystem (e.g., DataFrames and ML Pipelines), and improving both the scalability and ease of use of stream processing and machine learning.

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Building applications that can read and analyze a wide variety of data may change the way we do science and make business decisions. However, building such applications is challenging: real world data is expressed in natural language, images, or other “dark” data formats which are fraught with imprecision and ambiguity and so are difficult for machines to understand. This talk will describe Snorkel, whose goal is to make routine Dark Data and other prediction tasks dramatically easier. At its core, Snorkel focuses on a key bottleneck in the development of machine learning systems: the lack of large training datasets. In Snorkel, a user implicitly creates large training sets by writing simple programs that label data, instead of performing manual feature engineering or tedious hand-labeling of individual data items. We’ll provide a set of tutorials that will allow folks to write Snorkel applications that use Spark. Snorkel is open source on github and available from Snorkel.Stanford.edu.

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The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: * optimizing cluster setup; * configuring the cluster; * ingesting data; and * monitoring long-running jobs. We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters. Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.

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Spark Summit East Keynote by Ion Stoica A long-standing grand challenge in computing is to enable machines to act autonomously and intelligently: to rapidly and repeatedly take appropriate actions based on information in the world around them. To address this challenge, at UC Berkeley we are starting a new five year effort that focuses on the development of data-intensive systems that provide Real-Time Intelligence with Secure Execution (RISE). Following in the footsteps of AMPLab, RISELab is an interdisciplinary effort bringing together researchers across AI, robotics, security, and data systems. In this talk I’ll present our research vision and then discuss some of the applications that will be enabled by RISE technologies.

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g4dpvqap0

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The Building Blocks of QuestDB, a Time Series Database

javier ramirez

Talk Delivered at Valencia Codes Meetup 2024-06. Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds. It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.

06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...

Timothy Spann

06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI Discussion on Vector Databases, Unstructured Data and AI https://www.meetup.com/unstructured-data-meetup-new-york/ This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.

Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...

Subhajit Sahu

Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.

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ewymefz

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一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理

ahzuo

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一比一原版(NYU毕业证)纽约大学毕业证成绩单

ewymefz

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一比一原版(UofS毕业证书)萨省大学毕业证如何办理

v3tuleee

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Machine learning and optimization techniques for electrical drives.pptx

balafet

Malana- Gimlet Market Analysis (Portfolio 2)

TravisMalana

原版制作(Deakin毕业证书)迪肯大学毕业证学位证一模一样

u86oixdj

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原版制作(Deakin毕业证书)迪肯大学毕业证学位证一模一样

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2. The Dawn of Advanced Analytics 2 WatsonSIRI/assistantsSelf-driving cars Not just sci-fi, important applications for businesses

3. Analytics Transforming Industries 3 Predictive analytics Anomaly Detection Predict Product Revenue Customer Assessment Targeted Advertising Fraud Detection Risk Assessment Equipment Failure Data-Driven Real-time Analytics Applications

4. Today’s Data Reality 4 HADOOP DATA LAKES DATA HUBS CLOUD STORAGE DATA WAREHOUSE S Siloed, Fast-Growing Size, Cost

5. The Analytics Gap 5 IndustrialMediaPharma HADOOP DATA LAKES DATA HUBS CLOUD STORAGE DATA WAREHOUSES Siloed, Fast-Growing Size, Cost Real-time Data-Driven Analytics Applications

6. Why is there a gap? 6 Real-time Data-Driven Analytics Applications Manage Data infrastructure • Create, tune, monitor compute clusters. • Securely access silos of disparate data sources. • Enforce proper data governance. •1 Empower teams to be productive • Securely share big data clusters among analysts. • Interactively explore data and prototype ideas. • Debug, troubleshoot, version-control big data applications.• • • 2 Establish Production- Ready Applications • Setup robust data pipelines for ETL/ELT. • Productionize real-time applications with HA, FT. • Build, serve, maintain advanced machine learning models. • 3 Siloed, Fast-Growing Size, Cost

7. Databricks Cloud-Hosted Platform 7 • Separate compute & storage • Integrate existing data stores • Efficient cache on first access Just-in-Time Data Platform 1 Agile • Workflow scheduler for ML, streaming, SQL, ETL • High availability, fault- tolerant, performance- optimized Automated Apache Spark Management 3 Production-Ready • Interactive notebooks, dashboards, reports • Real-time exploration, machine learning, graph use cases Integrated Workspace 2 Democratize Big Data

8. HADOOP / DATA LAKES DATA WAREHOUSE S YOUR STORAGE CLOUD STORAGE 8 Databricks Just-in-Time Data Platform INTEGRATED WORKSPACEDASHBOARD S Reports NOTEBOOKS github, viz, collaboration BI TOOLS JUST-IN-TIME PROCESSING POWERED BY APACHE CLUSTERS: Auto-scaled, resilient, multi-tenant DATA INTEGRATION: secure and fast data source integrations INTERFACES: REST APIs & BI tools DATABRICKS SERVICES + YOUR CUSTOM SPARK APPS PRODUCTION JOBS DATA LAKE DATA HUB

9. The Challenge of Securing Analytics 9 End-to-end security a challenge for enterprises Securing file management Secure table management Secure cluster management Secure job workflows Secure dashboards, report, notebook management Today there are piecemeal solutions, but no comprehensive solution

10. Databricks Enterprise Security (DBES) 10 Holistic end-to-end security for Data Analytics Tables Clusters Workflow s Notebooks, Dashboards, Reports Files • Role-based access control • Auditing and governance • Integrated identity-management • Encryption on-disk and on-the- wire DBES provides The First End-to-End Security Solution for Apache Spark

11. Enterprise use-cases 11 Preventing credit card fraud Predict energy demand based on massive weather data Predict player churn, predicting network outages Natural language processing to extract author graph Generating tailored programs based on big data

12. Thank you.

13. Try Apache Spark with Databricks 13 http://databricks.com/try Try latest version of Apache Spark and preview of Spark 2.0

Editor's Notes

I HAVE AN ANNOUNCEMENT TODAY BUT FIRST, WANT TO TALK ABOUT DATABRICKS
THIS IS NOT JUST SCI-FI
Establish production-quality infrastructure
1: Global publisher Elsevier – team in US and EU perform natural language processing on all their content to experiment with new product ideas. 2: Energy analytics company DNV GL – Databricks sped up analytics of IoT data from weather and grid sensor by 100x. 3: Financial service provider LendUp– Databricks enabled them to update their machine models daily instead of weekly.
OPEN SOURCE

Disrupting Big Data with Apache Spark in the Cloud

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Editor's Notes