hbaseconasia2019 BigData NoSQL System: ApsaraDB, HBase and SparkMichael Stack
Wei Li of Alibaba
Track 2: Ecology and Solutions
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
Big Data Day LA 2015 - Introducing N1QL: SQL for Documents by Jeff Morris of ...Data Con LA
NoSQL has exploded on the developer scene promising alternatives to RDBMS that make rapidly developing, Internet scale applications easier than ever. However, as a trade off to the ease of development and scale, some of the familiarity with other well-known query interfaces such as SQL, has been lost. Until now that is...N1QL (pronounced ‘N1QL’) is a SQL like query language for querying JSON, which brings the familiarity of RDBMS back to the NoSQL world. In this session you will learn about the syntax and basics of this new language as well as Integration with the Couchbase SDKs.
Extending Spark SQL 2.4 with New Data Sources (Live Coding Session)Databricks
Spark SQL 2.4.x gives you two Data Source APIs that your structured queries can use to access data in custom formats, possibly in unsupported storage systems. There is the older and almost legacy DataSource API V1 and what you can consider a modern DataSource API V2. This talk will introduce you to the main entities of each DataSource API and show you the steps how to write a new data source live on stage. That should give you enough knowledge on expanding available data sources in Spark SQL with new ones.
The Future of Hadoop by Arun Murthy, PMC Apache Hadoop & Cofounder HortonworksData Con LA
Arun Murthy will be discussing the future of Hadoop and the next steps in what the big data world would start to look like in the future. With the advent of tools like Spark and Flink and containerization of apps using Docker, there is a lot of momentum currently in this space. Arun will share his thoughts and ideas on what the future holds for us.
Bio:-
Arun C. Murthy
Arun is a Apache Hadoop PMC member and has been a full time contributor to the project since the inception in 2006. He is also the lead of the MapReduce project and has focused on building NextGen MapReduce (YARN). Prior to co-founding Hortonworks, Arun was responsible for all MapReduce code and configuration deployed across the 42,000+ servers at Yahoo!. In essence, he was responsible for running Apache Hadoop’s MapReduce as a service for Yahoo!. Also, he jointly holds the current world sorting record using Apache Hadoop. Follow Arun on Twitter: @acmurthy.
Big Data Day LA 2015 - NoSQL: Doing it wrong before getting it right by Lawre...Data Con LA
The team at Fandango heartily embraced NoSQL, using Couchbase to power a key media publishing system. The initial implementation was fraught with integration issues and high latency, and required a major effort to successfully refactor. My talk will outline the key organizational and architectural decisions that created deep systemic problems, and the steps taken to re-architect the system to achieve a high level of performance at scale.
hbaseconasia2019 BigData NoSQL System: ApsaraDB, HBase and SparkMichael Stack
Wei Li of Alibaba
Track 2: Ecology and Solutions
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
Big Data Day LA 2015 - Introducing N1QL: SQL for Documents by Jeff Morris of ...Data Con LA
NoSQL has exploded on the developer scene promising alternatives to RDBMS that make rapidly developing, Internet scale applications easier than ever. However, as a trade off to the ease of development and scale, some of the familiarity with other well-known query interfaces such as SQL, has been lost. Until now that is...N1QL (pronounced ‘N1QL’) is a SQL like query language for querying JSON, which brings the familiarity of RDBMS back to the NoSQL world. In this session you will learn about the syntax and basics of this new language as well as Integration with the Couchbase SDKs.
Extending Spark SQL 2.4 with New Data Sources (Live Coding Session)Databricks
Spark SQL 2.4.x gives you two Data Source APIs that your structured queries can use to access data in custom formats, possibly in unsupported storage systems. There is the older and almost legacy DataSource API V1 and what you can consider a modern DataSource API V2. This talk will introduce you to the main entities of each DataSource API and show you the steps how to write a new data source live on stage. That should give you enough knowledge on expanding available data sources in Spark SQL with new ones.
The Future of Hadoop by Arun Murthy, PMC Apache Hadoop & Cofounder HortonworksData Con LA
Arun Murthy will be discussing the future of Hadoop and the next steps in what the big data world would start to look like in the future. With the advent of tools like Spark and Flink and containerization of apps using Docker, there is a lot of momentum currently in this space. Arun will share his thoughts and ideas on what the future holds for us.
Bio:-
Arun C. Murthy
Arun is a Apache Hadoop PMC member and has been a full time contributor to the project since the inception in 2006. He is also the lead of the MapReduce project and has focused on building NextGen MapReduce (YARN). Prior to co-founding Hortonworks, Arun was responsible for all MapReduce code and configuration deployed across the 42,000+ servers at Yahoo!. In essence, he was responsible for running Apache Hadoop’s MapReduce as a service for Yahoo!. Also, he jointly holds the current world sorting record using Apache Hadoop. Follow Arun on Twitter: @acmurthy.
Big Data Day LA 2015 - NoSQL: Doing it wrong before getting it right by Lawre...Data Con LA
The team at Fandango heartily embraced NoSQL, using Couchbase to power a key media publishing system. The initial implementation was fraught with integration issues and high latency, and required a major effort to successfully refactor. My talk will outline the key organizational and architectural decisions that created deep systemic problems, and the steps taken to re-architect the system to achieve a high level of performance at scale.
This is an exam cheat sheet hopes to cover all keys points for GCP Data Engineer Certification Exam
Let me know if there is any mistake and I will try to update it
You run your SQL-centric infrastructure for 10 years and slowly starting to note you can’t do this way anymore – everything is getting too expensive but your business requires things which are simply impossible without radical changes.
This is exact situation we had 2 years before. So we’d like to show our experience:
- Why and how we came into Big Data?
- Why we choose Apache and Hadoop?
- What to do and what is already done?
- What lessons were learned?
- Hadoop and relational databases: fight or synergy?
- Reactive Big Data manifest.
The strategic relationship between Hortonworks and SAP enables SAP to resell Hortonworks Data Platform (HDP) and provide enterprise support for their global customer base. This means SAP customers can incorporate enterprise Hadoop as a complement within a data architecture that includes SAP HANA and SAP BusinessObjects enabling a broad range of new analytic applications.
DoneDeal AWS Data Analytics Platform build using AWS products: EMR, Data Pipeline, S3, Kinesis, Redshift and Tableau. Custom built ETL was written using PySpark.
Building Real-Time BI Systems with Kafka, Spark, and Kudu: Spark Summit East ...Spark Summit
One of the key challenges in working with real-time and streaming data is that the data format for capturing data is not necessarily the optimal format for ad hoc analytic queries. For example, Avro is a convenient and popular serialization service that is great for initially bringing data into HDFS. Avro has native integration with Flume and other tools that make it a good choice for landing data in Hadoop. But columnar file formats, such as Parquet and ORC, are much better optimized for ad hoc queries that aggregate over large number of similar rows.
Today's organizations contend with more diverse applications, data, and systems than ever before – silos that are often fragmented and difficult to leverage together. iWay Big Data Integrator (BDI) simplifies the creation, management, and use of Hadoop-based data lakes. It provides a modern, native approach to Hadoop-based data integration and management that ensures high levels of capability, compatibility, and flexibility to help your organization.
Join us to learn how you can simplify adoption of Apache Hadoop using iWay Big Data Integrator. Learn about our ability to streamline the deployment of ingestion, transformation, and extraction tasks.
See the pre-recorded webcast online at: http://www.informationbuilders.com/webevents/online/24427#sthash.J0cRy1PG.dpuf
The strategic relationship between Hortonworks and SAP enables SAP to resell Hortonworks Data Platform (HDP) and provide enterprise support for their global customer base. This means SAP customers can incorporate enterprise Hadoop as a complement within a data architecture that includes SAP HANA, Sybase and SAP BusinessObjects enabling a broad range of new analytic applications.
Design for Scale - Building Real Time, High Performing Marketing Technology p...Amazon Web Services
DynamoDB presented by David Pearson from AWS
Bizo Business Audience Marketing success story on AWS by Alex Boisvert, Director of Engineering, Bizo
In today's world, consumer habits change fast and marketing decisions need to be made within seconds, not days. Delivering engaging advertising experiences requires real time, high performing architectures that provide digital advertisers the ability to measure and improve the performance of their campaigns and tie them more closely to corporate goals. The insights gleaned from the massive amounts of data collected can then be used to dynamically adjust media spend and creative execution for optimal performance. The AWS Cloud enables you to deliver marketing content and advertisements with the levels of availability, performance, and personalization that your customers expect. Plus, AWS lowers your costs. Join us to learn about how big data and low latency / high performing architectures are changing the game for digital advertising.
Using Oracle Big Data SQL 3.0 to add Hadoop & NoSQL to your Oracle Data Wareh...Mark Rittman
As presented at OGh SQL Celebration Day in June 2016, NL. Covers new features in Big Data SQL including storage indexes, storage handlers and ability to install + license on commodity hardware
This is a slide deck that was used for our 11/19/15 Nike Tech Talk to give a detailed overview of the SnappyData technology vision. The slides were presented by Jags Ramnarayan, Co-Founder & CTO of SnappyData
Gluent New World #02 - SQL-on-Hadoop : A bit of History, Current State-of-the...Mark Rittman
Hadoop and NoSQL platforms initially focused on Java developers and slow but massively-scalable MapReduce jobs as an alternative to high-end but limited-scale analytics RDBMS engines. Apache Hive opened-up Hadoop to non-programmers by adding a SQL query engine and relational-style metadata layered over raw HDFS storage, and since then open-source initiatives such as Hive Stinger, Cloudera Impala and Apache Drill along with proprietary solutions from closed-source vendors have extended SQL-on-Hadoop’s capabilities into areas such as low-latency ad-hoc queries, ACID-compliant transactions and schema-less data discovery – at massive scale and with compelling economics.
In this session we’ll focus on technical foundations around SQL-on-Hadoop, first reviewing the basic platform Apache Hive provides and then looking in more detail at how ad-hoc querying, ACID-compliant transactions and data discovery engines work along with more specialised underlying storage that each now work best with – and we’ll take a look to the future to see how SQL querying, data integration and analytics are likely to come together in the next five years to make Hadoop the default platform running mixed old-world/new-world analytics workloads.
Big Data for Oracle Devs - Towards Spark, Real-Time and Predictive AnalyticsMark Rittman
This is a session for Oracle DBAs and devs that looks at the cutting edge big data techs like Spark, Kafka etc, and through demos shows how Hadoop is now a a real-time platform for fast analytics, data integration and predictive modeling
Jason Huang, Solutions Engineer, Qubole at MLconf ATL - 9/18/15MLconf
Sparking Data in the Cloud: Data isn’t useful until it’s used to drive decision-making. Companies, like Pinterest, are using Machine Learning to build data-driven recommendation engines and perform advanced cluster analysis. In this talk, Praveen Seluka will cover best practices for running Spark in the cloud, common challenges in iterative design and interactive analysis.
This is an exam cheat sheet hopes to cover all keys points for GCP Data Engineer Certification Exam
Let me know if there is any mistake and I will try to update it
You run your SQL-centric infrastructure for 10 years and slowly starting to note you can’t do this way anymore – everything is getting too expensive but your business requires things which are simply impossible without radical changes.
This is exact situation we had 2 years before. So we’d like to show our experience:
- Why and how we came into Big Data?
- Why we choose Apache and Hadoop?
- What to do and what is already done?
- What lessons were learned?
- Hadoop and relational databases: fight or synergy?
- Reactive Big Data manifest.
The strategic relationship between Hortonworks and SAP enables SAP to resell Hortonworks Data Platform (HDP) and provide enterprise support for their global customer base. This means SAP customers can incorporate enterprise Hadoop as a complement within a data architecture that includes SAP HANA and SAP BusinessObjects enabling a broad range of new analytic applications.
DoneDeal AWS Data Analytics Platform build using AWS products: EMR, Data Pipeline, S3, Kinesis, Redshift and Tableau. Custom built ETL was written using PySpark.
Building Real-Time BI Systems with Kafka, Spark, and Kudu: Spark Summit East ...Spark Summit
One of the key challenges in working with real-time and streaming data is that the data format for capturing data is not necessarily the optimal format for ad hoc analytic queries. For example, Avro is a convenient and popular serialization service that is great for initially bringing data into HDFS. Avro has native integration with Flume and other tools that make it a good choice for landing data in Hadoop. But columnar file formats, such as Parquet and ORC, are much better optimized for ad hoc queries that aggregate over large number of similar rows.
Today's organizations contend with more diverse applications, data, and systems than ever before – silos that are often fragmented and difficult to leverage together. iWay Big Data Integrator (BDI) simplifies the creation, management, and use of Hadoop-based data lakes. It provides a modern, native approach to Hadoop-based data integration and management that ensures high levels of capability, compatibility, and flexibility to help your organization.
Join us to learn how you can simplify adoption of Apache Hadoop using iWay Big Data Integrator. Learn about our ability to streamline the deployment of ingestion, transformation, and extraction tasks.
See the pre-recorded webcast online at: http://www.informationbuilders.com/webevents/online/24427#sthash.J0cRy1PG.dpuf
The strategic relationship between Hortonworks and SAP enables SAP to resell Hortonworks Data Platform (HDP) and provide enterprise support for their global customer base. This means SAP customers can incorporate enterprise Hadoop as a complement within a data architecture that includes SAP HANA, Sybase and SAP BusinessObjects enabling a broad range of new analytic applications.
Design for Scale - Building Real Time, High Performing Marketing Technology p...Amazon Web Services
DynamoDB presented by David Pearson from AWS
Bizo Business Audience Marketing success story on AWS by Alex Boisvert, Director of Engineering, Bizo
In today's world, consumer habits change fast and marketing decisions need to be made within seconds, not days. Delivering engaging advertising experiences requires real time, high performing architectures that provide digital advertisers the ability to measure and improve the performance of their campaigns and tie them more closely to corporate goals. The insights gleaned from the massive amounts of data collected can then be used to dynamically adjust media spend and creative execution for optimal performance. The AWS Cloud enables you to deliver marketing content and advertisements with the levels of availability, performance, and personalization that your customers expect. Plus, AWS lowers your costs. Join us to learn about how big data and low latency / high performing architectures are changing the game for digital advertising.
Using Oracle Big Data SQL 3.0 to add Hadoop & NoSQL to your Oracle Data Wareh...Mark Rittman
As presented at OGh SQL Celebration Day in June 2016, NL. Covers new features in Big Data SQL including storage indexes, storage handlers and ability to install + license on commodity hardware
This is a slide deck that was used for our 11/19/15 Nike Tech Talk to give a detailed overview of the SnappyData technology vision. The slides were presented by Jags Ramnarayan, Co-Founder & CTO of SnappyData
Gluent New World #02 - SQL-on-Hadoop : A bit of History, Current State-of-the...Mark Rittman
Hadoop and NoSQL platforms initially focused on Java developers and slow but massively-scalable MapReduce jobs as an alternative to high-end but limited-scale analytics RDBMS engines. Apache Hive opened-up Hadoop to non-programmers by adding a SQL query engine and relational-style metadata layered over raw HDFS storage, and since then open-source initiatives such as Hive Stinger, Cloudera Impala and Apache Drill along with proprietary solutions from closed-source vendors have extended SQL-on-Hadoop’s capabilities into areas such as low-latency ad-hoc queries, ACID-compliant transactions and schema-less data discovery – at massive scale and with compelling economics.
In this session we’ll focus on technical foundations around SQL-on-Hadoop, first reviewing the basic platform Apache Hive provides and then looking in more detail at how ad-hoc querying, ACID-compliant transactions and data discovery engines work along with more specialised underlying storage that each now work best with – and we’ll take a look to the future to see how SQL querying, data integration and analytics are likely to come together in the next five years to make Hadoop the default platform running mixed old-world/new-world analytics workloads.
Big Data for Oracle Devs - Towards Spark, Real-Time and Predictive AnalyticsMark Rittman
This is a session for Oracle DBAs and devs that looks at the cutting edge big data techs like Spark, Kafka etc, and through demos shows how Hadoop is now a a real-time platform for fast analytics, data integration and predictive modeling
Jason Huang, Solutions Engineer, Qubole at MLconf ATL - 9/18/15MLconf
Sparking Data in the Cloud: Data isn’t useful until it’s used to drive decision-making. Companies, like Pinterest, are using Machine Learning to build data-driven recommendation engines and perform advanced cluster analysis. In this talk, Praveen Seluka will cover best practices for running Spark in the cloud, common challenges in iterative design and interactive analysis.
Managing data analytics in a hybrid cloudKaran Singh
We’ll talk about the changes in the industry that customers are faced with and how Red Hat Hyperconverged Infrastructure can address those challenges . Our customers are struggling not only to manage the growth of big data (structured and unstructured), but also to reap timely business insights from their data using their existing data infrastructure like monolithic Hadoop clusters. This often leads to alternative approaches that often lead to disappointing results.
http://bit.ly/1BTaXZP – As organizations look for even faster ways to derive value from big data, they are turning to Apache Spark is an in-memory processing framework that offers lightning-fast big data analytics, providing speed, developer productivity, and real-time processing advantages. The Spark software stack includes a core data-processing engine, an interface for interactive querying, Spark Streaming for streaming data analysis, and growing libraries for machine-learning and graph analysis. Spark is quickly establishing itself as a leading environment for doing fast, iterative in-memory and streaming analysis. This talk will give an introduction the Spark stack, explain how Spark has lighting fast results, and how it complements Apache Hadoop. By the end of the session, you’ll come away with a deeper understanding of how you can unlock deeper insights from your data, faster, with Spark.
Les mégadonnées représentent un vrai enjeu à la fois technique, business et de société
: l'exploitation des données massives ouvre des possibilités de transformation radicales au
niveau des entreprises et des usages. Tout du moins : à condition que l'on en soit
techniquement capable... Car l'acquisition, le stockage et l'exploitation de quantités
massives de données représentent des vrais défis techniques.
Une architecture big data permet la création et de l'administration de tous les
systèmes techniques qui vont permettre la bonne exploitation des données.
Il existe énormément d'outils différents pour manipuler des quantités massives de
données : pour le stockage, l'analyse ou la diffusion, par exemple. Mais comment assembler
ces différents outils pour réaliser une architecture capable de passer à l'échelle, d'être
tolérante aux pannes et aisément extensible, tout cela sans exploser les coûts ?
Le succès du fonctionnement de la Big data dépend de son architecture, son
infrastructure correcte et de son l’utilité que l’on fait ‘’ Data into Information into Value ‘’.
L’architecture de la Big data est composé de 4 grandes parties : Intégration, Data Processing
& Stockage, Sécurité et Opération.
As data continues to pile up and departments find new ways to look at it, your datacenter needs a dense, powerful solution that can analyze this data quickly and scale resources as needed.
The Scalable Modular Server DX2000 from NEC processed big data quickly as we added server nodes and a second enclosure. In our k-means data cluster analysis test, a two-enclosure DX2000 solution running 85 Apache Spark executors and Red Hat Enterprise Linux OpenStack Platform processed 100 GB in just 46 seconds.
If you’re looking to expand your business through data analysis, the Scalable Modular Server DX2000 from NEC powered by Intel and running Apache Spark can help you unlock key big data insights.
Deploy Apache Spark™ on Rackspace OnMetal™ for Cloud Big Data PlatformRackspace
There's an elephant in the room when it comes to Big Data. Apache Hadoop and Spark offer the promise to transform how businesses leverage Big Data, finding the right mix of flexible deployments, elastic scalability, and performance can be daunting.
Introducing Rackspace OnMetal™ for Apache Spark™ an industry first that combines the performance and efficiency of bare metal with the ease and flexibility of cloud. With Rackspace OnMetal for Cloud Big Data Platform you can transform how you run Hadoop and Spark workloads:
•Deploy in minutes, not months
•Spin instances up or down on demand
•Process data in-memory for faster query times
•Get bare metal performance and say goodbye to virtualization taxes
Sign up and learn how Rackspace OnMetal for Cloud Big Data Platform can rapidly move your organization from planning to deploying.
Uncovering an Apache Spark 2 Benchmark - Configuration, Tuning and Test ResultsDataWorks Summit
Apache Spark is increasingly adopted as an alternate processing framework to MapReduce, due to its ability to speed up batch, interactive and streaming analytics. Spark enables new analytics use cases like machine learning and graph analysis with its rich and easy to use programming libraries. And, it offers the flexibility to run analytics on data stored in Hadoop, across data across object stores and within traditional databases. This makes Spark an ideal platform for accelerating cross-platform analytics on-premises and in the cloud. Building on the success of Spark 1.x release, Spark 2.x delivers major improvements in the areas of API, Performance, and Structured Streaming. In this paper, we will cover a high-level view of the Apache Spark framework, and then focus on what we consider to be very important improvements made in Apache Spark 2.x. We will then share the results of a real-world benchmark effort and share details on Spark and environment configuration changes made to our lab, discuss the results of the benchmark, and provide a reference architecture example for those interested in taking Spark 2.x for their own test drive. This presentation stresses the value of refreshing the Spark 1 with Spark 2 as performance testing results show 2.3x improvement with SparkSQL workloads similar to TPC Benchmark™ DS (TPC-DS). MARK LOCHBIHLER, Principal Architect, Hortonworks and VIPLAVA MADASU, Big Data Systems Engineer, Hewlett Packard Enterprise
Data Science for Beginner by Chetan Khatri and Deptt. of Computer Science, Ka...Chetan Khatri
What is Data Science?
What is Machine Learning, Deep Learning, and AI?
Motivation
Philosophy of Artificial Intelligence (AI)
Role of AI in Daily life
Use cases/Applications
Tools & Technologies
Challenges: Bias, Fake Content, Digital Psychography, Security
Detect Fake Content with “AI”
Learning Path
Career Path
Demystify Information Security & Threats for Data-Driven Platforms With Cheta...Chetan Khatri
Pragmatic presentation on Penetration testing for Data-Driven Platforms.
Agenda:
- Motivation
- Information Security - Ethics.
- Encryption
- Authentication
- Information Security & Potential threats with Open Source World.
- Find vulnerabilities.
- Checklist before using any Open Source library.
- Vulnerabilities report.
- Penetration Testing for Data-Driven Developments.
ScalaTo July 2019 - No more struggles with Apache Spark workloads in productionChetan Khatri
Scala Toronto July 2019 event at 500px.
Pure Functional API Integration
Apache Spark Internals tuning
Performance tuning
Query execution plan optimisation
Cats Effects for switching execution model runtime.
Discovery / experience with Monix, Scala Future.
No more struggles with Apache Spark workloads in productionChetan Khatri
Paris Scala Group Event May 2019, No more struggles with Apache Spark workloads in production.
Apache Spark
Primary data structures (RDD, DataSet, Dataframe)
Pragmatic explanation - executors, cores, containers, stage, job, a task in Spark.
Parallel read from JDBC: Challenges and best practices.
Bulk Load API vs JDBC write
An optimization strategy for Joins: SortMergeJoin vs BroadcastHashJoin
Avoid unnecessary shuffle
Alternative to spark default sort
Why dropDuplicates() doesn’t result consistency, What is alternative
Optimize Spark stage generation plan
Predicate pushdown with partitioning and bucketing
Why not to use Scala Concurrent ‘Future’ explicitly!
No more struggles with Apache Spark (PySpark) workloads in production, Chetan Khatri, Data Science Practice Leader.
Accionlabs India. PyconLT’19, May 26 - Vilnius Lithuania
Fossasia ai-ml technologies and application for product development-chetan kh...Chetan Khatri
Train at GPU and Inference at Mobile, Artificial Intelligence / Machine learning Technologies and Applications for AI Driven Product Development. Talk at FOSSASIA 2018, Singapore
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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).
HBaseConAsia 2018 - Scaling 30 TB's of Data lake with Apache HBase and Scala DSL at Production
1. hosted by
Scaling 30 TB’s of Data Lake
with Apache HBase and Scala
DSL at Production
Chetan Khatri
2. hosted by
Who Am I
Lead - Data Science, Technology Evangelist @ Accion labs India Pvt. Ltd.
Contributor @ Apache Spark, Apache HBase, Elixir Lang, Spark HBase
Connectors.
Co-Authored University Curriculum @ University of Kachchh, India.
Data Engineering @: Nazara Games, Eccella Corporation.
Advisor - Data Science Lab, University of Kachchh, India.
M.Sc. - Computer Science from University of Kachchh, India.
3. hosted by
Agenda 01
02
04
03
What is Apache HBase
Why Apache HBase
Apache Spark and Scala
Apache Spark HBase Connector
05 Case Study: Retail Analytics
Architecturing Fast Data Processing Platform to
Scale 30 TB Data in Production
4. hosted by
Add the title
• Modules do not limit the size,
number, interval, can be adjusted
according to need
• Modules do not limit the size,
number, interval, can be adjusted
according to need
• Modules do not limit the size,
number, interval, can be adjusted
according to need
Source: https://hbase.apache.org/
● Column-oriented NoSQL
● Non-relational
● Distributed database build on top of HDFS.
● Modeled after Google’s BigTable.
● Built for fault-tolerant application with billions/
trillions of rows and millions of columns.
● Very low latency and near real-time random
reads and random writes.
● Replication, end-to-end checksums,
automatic rebalancing with HDFS.
● Compression
● Bloom filters
● MapReduce over HBase data.
● Best at fetching rows by key, scanning
ranges of rows with ordered partitioning.
What is Apache HBase
5. hosted by
What is Apache Spark ?
Source: https://spark.apache.org/
Structured Data / SQL -
Spark SQL
Graph Processing -
GraphX
Machine Learning -
MLlib
Streaming - Spark Streaming,
Structured Streaming
6. hosted by
What is Scala
● Scala is a modern multi-paradigm programming language
designed to express common programming patterns in a concise,
elegant, and type-safe way.
● Scala is object-oriented
● Scala is functional
● Strongly typed, Type Inference
● Higher Order Functions
● Lazy Computation
Source: www.scala-lang.org
7. hosted by
Case Story: Retail Analytics
Architecting Fast Data Processing Platform to Scale 30 TB of Data in Production
Use cases in Retail Analytics:
Business: explain the who, what, when, where, why and how they are doing
Retailing.
● What is selling as compared to what was being ordered.
● Effective promotions - right promotions at right outlet and right time.
● What types of Cigarette consumers are shopping in your outlets ?
○ Gives smoking patterns in specific geography, predict demand on supply.
● What are the purchasing patterns of your consumers ?
○ are they purchasing Pizza and Ice cream together ?
○ are they purchasing multiple Instant food products with soda together ?
● Time Series problem - year, month, day of year, week of year to Identify which
brands are not getting sold at specific geography, so it can be swap to other
store.
8. hosted by
Case Story: Retail Analytics - Scale
Challenges
² Weekly Data refresh, Daily Data refresh batch Spark / Hadoop job
execution failures with unutilized Spark Cluster.
² Scalability of massive data:
○ ~4.6 Billion events on every weekly data refresh.
○ Processing historical data: one time bulk load ~30 TB of data / ~17
billion transactional records.
² Linear / sequential execution mode with broken data pipelines.
² Joining ~17 billion transactional records with skewed data nature.
² Data deduplication - outlet, item at retail.
9. hosted by
Using HBase as a MDM System
MDM - Master Data Management
1. HBase Driven Data Deduplication Algorithms
Example,
² Outlet Matching
² Item Matching
² Address Matching
² Brand Matching
2. Abbreviation Standardization
Example,
² UOM Standardization
² Outlet Name, Address Standardization
² UPC Standardization
UOM Quantity
PACK 2
2PACK NA
2PK
10. hosted by
Using HBase as a MDM System
Data Deduplication Problem Retail Analytics !
Examples,
● You may find Item with same UPC code.
● You may find Outlet with same Outlet number.
● What if UPC Code gets upgraded from 10 Digits to 14 Digits.
(Update everywhere, needs faster update.)
11. hosted by
HBase - NoSQL, Denormalized columnar schema model
For example,
Table: transactional_line_item
Column Family: f Column Family: o Column Family: t
created_datetime
file_id
transaction_id
manufacturer_operator_submitter_id
Outlet_id
Outlet_name
Outlet_state
Outlet_city
Outlet_address1
Outlet_address2
Outlet_region
Outlet_country
Outlet_owner_name
Outlet_status
Outlet_zipcode
Outlet_started_date
outlet_sub_chain_name
Transaction_id
Item_id
Promo_code
Gross_price
Discount
Quantity
Upc
Uom
State_gst
Country_gst
Delivery_charges
Packing_charges
Vendor_discount
Partner_discount
Corporate_discount
reward_point_discount
12. hosted by
Case Story: Retail Analytics - Scale
- 5x performance improvements by re-engineering entire data lake to analytical engine
pipeline’s.
- Proposed highly concurrent, elastic, non-blocking, asynchronous architecture to save
customer’s ~22 hours runtime (~8 hours from 30 hours) for 4.6 Billion events.
- 10x performance improvements on historical load by under the hood algorithms
optimization on 17 Billion events (Benchmarks ~1 hour execution time only)
- Master Data Management (MDM) - Deduplication and fuzzy logic matching on retails
data(Item, Outlet) improved elastic performance.
- Using HBase as a Master Data Management (MDM) System.
16. hosted by
Data Processing Infrastructure
● MapR Distribution - http://archive.mapr.com/releases/
ecosystem-5.x/redhat/
● Data Lake - Apache HBase 0.98.12
● EDW / Analytical Data store - Apache Hive 1.2.1
● Unified Execution Engine - Apache Spark 2.0.1
● Distributed File storage - MapR-FS
● Queueing mechanism - Apache Kafka
● Streaming - HTTP Akka + scala 2.11 , Spark Streaming
● Reporting Database - PostgreSQL 9.x
● Legacy Database - Oracle 9
● Workflow Management tool - BMC Control M
17. hosted by
Rethink
- Fast Data Architectures. Unify, Simplify.
UNIFIED fast data processing engine that provides:
The
SCALE
of data lake
The
RELIABILITY &
PERFORMANCE
of data warehouse
The
LOW LATENCY
of streaming
18. hosted by
Spark HBase Connector.
Credit: Contributors
https://github.com/nerdammer/spark-hbase-
connector
19. hosted by
Spark HBase Connector.
It’s a Spark package connector written on top of Java HBase API. A
simple and elegant way to write Spark - HBase Jobs. Powerful
Functional Scala DSL integrated for Apache Spark.
Supports:
● Scala > 2.10
● Spark > 1.6
● HBase > 1.0
20. hosted by
Spark HBase Connector.
Dependency in build.sbt - libraryDependencies += "it.nerdammer.bigdata" % "spark-hbase-connector_2.10" % "1.0.3"
Setting the HBase Host