The document summarizes Presto's development over the past 10 months, current capabilities, and future plans. Presto is a distributed SQL query engine used at Facebook to query large datasets. Over the past 10 months it saw 30 releases, contributions from 42 developers, and optimizations that improved query performance by 50-300%. Facebook uses Presto to scan petabytes of data daily and process trillions of rows. Future plans include new SQL features, connectors, security improvements, and optimizing the planner and execution engine.
Presto, an open source distributed SQL engine originally built at Facebook, has a rapidly growing community of developers and users. In this talk, speakers from both Facebook and Teradata, will discuss technical details of some of the recent developments such as integration with Hadoop ecosystem (YARN/Slider and Ambari), security features (Kerberos), enabling BI tools via JDBC/ODBC drivers, new connectors (Redis, MongoDB) and storage engines (Raptor) as well as improvements in performance and ANSI SQL coverage. In addition, we will present a few use cases and major new users that leverage interactive SQL capabilities Presto offers. Finally, we will present our roadmap for the next year.
See the video at https://youtu.be/wMy3LXuTb0U
Presto, an open source distributed SQL engine originally built at Facebook, has a rapidly growing community of developers and users. In this talk, speakers from both Facebook and Teradata, will discuss technical details of some of the recent developments such as integration with Hadoop ecosystem (YARN/Slider and Ambari), security features (Kerberos), enabling BI tools via JDBC/ODBC drivers, new connectors (Redis, MongoDB) and storage engines (Raptor) as well as improvements in performance and ANSI SQL coverage. In addition, we will present a few use cases and major new users that leverage interactive SQL capabilities Presto offers. Finally, we will present our roadmap for the next year.
See the video at https://youtu.be/wMy3LXuTb0U
One of the key differences between Presto and Hive, also a crucial functional requirement Facebook made when launching this new SQL engine project, was to have the opportunity to query different kinds of data sources via a uniform ANSI SQL interface.
Presto, an open source distributed analytical SQL engine, implements this with it’s connector architecture, creating an abstraction layer for anything that can be expressed as in a row-like format, ranging from MySQL tables, HDFS, Amazon S3 to NoSQL stores, Kafka streams and proprietary data sources. Presto connector SPI allows anyone to implement a Presto connector and benefit from the capabilities of the Presto SQL engine, enabling them to join data from various sources within a single SQL query.
Денис Резник "Моя база данных не справляется с нагрузкой. Что делать?"Fwdays
В течении доклада мы с вами рассмотрим ряд принципов и техник, которые позволят вашей базе данных справляться с большей нагрузкой. P.S. Все примеры и демо будут проводиться на базе данных MS SQL Server. Все совпадения с другими базами данными случайны, но вполне вероятны :) так что знания, полученные в ходе доклада, могут вам пригодиться даже если вы работаете с другой базой данных.
ELK Stack (Elasticsearch, Logstash, Kibana) as a Log-Management solution for the Microsoft developer presented at the .net Usergroup in Munich in June 2015.
Hoodie: How (And Why) We built an analytical datastore on SparkVinoth Chandar
Exploring a specific problem of ingesting petabytes of data in Uber and why they ended up building an analytical datastore from scratch using Spark. Then, discuss design choices and implementation approaches in building Hoodie to provide near-real-time data ingestion and querying using Spark and HDFS.
https://spark-summit.org/2017/events/incremental-processing-on-large-analytical-datasets/
Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.
(BDT303) Running Spark and Presto on the Netflix Big Data PlatformAmazon Web Services
In this session, we discuss how Spark and Presto complement the Netflix big data platform stack that started with Hadoop, and the use cases that Spark and Presto address. Also, we discuss how we run Spark and Presto on top of the Amazon EMR infrastructure; specifically, how we use Amazon S3 as our data warehouse and how we leverage Amazon EMR as a generic framework for data-processing cluster management.
One of the key differences between Presto and Hive, also a crucial functional requirement Facebook made when launching this new SQL engine project, was to have the opportunity to query different kinds of data sources via a uniform ANSI SQL interface.
Presto, an open source distributed analytical SQL engine, implements this with it’s connector architecture, creating an abstraction layer for anything that can be expressed as in a row-like format, ranging from MySQL tables, HDFS, Amazon S3 to NoSQL stores, Kafka streams and proprietary data sources. Presto connector SPI allows anyone to implement a Presto connector and benefit from the capabilities of the Presto SQL engine, enabling them to join data from various sources within a single SQL query.
Денис Резник "Моя база данных не справляется с нагрузкой. Что делать?"Fwdays
В течении доклада мы с вами рассмотрим ряд принципов и техник, которые позволят вашей базе данных справляться с большей нагрузкой. P.S. Все примеры и демо будут проводиться на базе данных MS SQL Server. Все совпадения с другими базами данными случайны, но вполне вероятны :) так что знания, полученные в ходе доклада, могут вам пригодиться даже если вы работаете с другой базой данных.
ELK Stack (Elasticsearch, Logstash, Kibana) as a Log-Management solution for the Microsoft developer presented at the .net Usergroup in Munich in June 2015.
Hoodie: How (And Why) We built an analytical datastore on SparkVinoth Chandar
Exploring a specific problem of ingesting petabytes of data in Uber and why they ended up building an analytical datastore from scratch using Spark. Then, discuss design choices and implementation approaches in building Hoodie to provide near-real-time data ingestion and querying using Spark and HDFS.
https://spark-summit.org/2017/events/incremental-processing-on-large-analytical-datasets/
Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.
(BDT303) Running Spark and Presto on the Netflix Big Data PlatformAmazon Web Services
In this session, we discuss how Spark and Presto complement the Netflix big data platform stack that started with Hadoop, and the use cases that Spark and Presto address. Also, we discuss how we run Spark and Presto on top of the Amazon EMR infrastructure; specifically, how we use Amazon S3 as our data warehouse and how we leverage Amazon EMR as a generic framework for data-processing cluster management.
Presentation on Presto (http://prestodb.io) basics, design and Teradata's open source involvement. Presented on Sept 24th 2015 by Wojciech Biela and Łukasz Osipiuk at the #20 Warsaw Hadoop User Group meetup http://www.meetup.com/warsaw-hug/events/224872317
Hello, Enterprise! Meet Presto. (Presto Boston Meetup 10062015)Matt Fuller
Teradata has been hard at work on Presto, and we want to share with you what we've done so far and our roadmap going forward. From presto-admin, a tool for installing and administering Presto, to YARN/Ambari support, to fully certified JDBC and ODBC drivers, we are committed to making Presto the best, most enterprise-ready SQL-on Hadoop solution out there.
Presto: Distributed SQL on Anything - Strata Hadoop 2017 San Jose, CAkbajda
Teradata joined the Presto community in 2015 and is now a leading contributor to this open source SQL engine, originally created by Facebook. The project has a rapidly growing community of users, including Airbnb, FINRA, Netflix, Twitter, and Uber. Kamil Bajda-Pawlikowski explores the key architectural components that allow querying variety of data sources and make Presto uniquely position to be applied in both Hadoop and Cloud use cases. Along the way, Kamil covers Teradata’s recent enhancements in query performance, security integrations, and ANSI SQL coverage and shares the roadmap for 2017 and beyond.
The Pisoni Family announces the 2014 Spring release of their Lucia wines. This release consists of the 2012 vintages of Lucia Chardonnay, Pinot Noir and Syrah from the Soberanes Vineyard, as well as the Santa Lucia Highlands cuvées.
One key area of Oracle OpenWorld 2016 was data in various shapes. Big Data, streaming data and traditional transactional data. The power of SQL to access and unleash all data - even data in NoSQL databases. The advent of the citizen data scientist. Streaming data analysis in real time on vast and fast and vast data, data discovery. And the new Oracle Database 12cR2 release. Forms, APEX, SQL and PL/SQL.
How Database Convergence Impacts the Coming Decades of Data ManagementSingleStore
How Database Convergence Impacts the Coming Decades of Data Management by Nikita Shamgunov, CEO and co-founder of MemSQL.
Presented at NYC Database Month in October 2017. NYC Database Month is the largest database meetup in New York, featuring talks from leaders in the technology space. You can learn more at http://www.databasemonth.com.
Solr Power FTW: Powering NoSQL the World OverAlex Pinkin
Solr is an open source, Lucene based search platform originally developed by CNET and used by the likes of Netflix, Yelp, and StubHub which has been rapidly growing in popularity and features during the last few years. Learn how Solr can be used as a Not Only SQL (NoSQL) database along the lines of Cassandra, Memcached, and Redis. NoSQL data stores are regularly described as non-relational, distributed, internet-scalable and are used at both Facebook and Digg. This presentation will quickly cover the fundamentals of NoSQL data stores, the basics of Lucene, and what Solr brings to the table. Following that we will dive into the technical details of making Solr your primary query engine on large scale web applications, thus relegating your traditional relational database to little more than a simple key store. Real solutions to problems like handling four billion requests per month will be presented. We'll talk about sizing and configuring the Solr instances to maintain rapid response times under heavy load. We'll show you how to change the schema on a live system with tens of millions of documents indexed while supporting real-time results. And finally, we'll answer your questions about ways to work around the lack of transactions in Solr and how you can do all of this in a highly available solution.
Building a Complex, Real-Time Data Management ApplicationJonathan Katz
Congratulations: you've been selected to build an application that will manage whether or not the rooms for PGConf.EU are being occupied by a session!
On the surface, this sounds simple, but we will be managing the rooms of PGConf.EU, so we know that a lot of people will be accessing the system. Therefore, we need to ensure that the system can handle all of the eager users that will be flooding the PGConf.EU website checking to see what availability each of the PGConf.EU rooms has.
To do this, we will explore the following PGConf.EU features:
* Data types and their functionality, such as:
* Data/Time types
* Ranges
Indexes such as:
* GiST
* SP-Gist
* Common Table Expressions and Recursion
* Set generating functions and LATERAL queries
* Functions and the PL/PGSQL
* Triggers
* Logical decoding and streaming
We will be writing our application primary with SQL, though we will sneak in a little bit of Python and using Kafka to demonstrate the power of logical decoding.
At the end of the presentation, we will have a working application, and you will be happy knowing that you provided a wonderful user experience for all PGConf.EU attendees made possible by the innovation of PGConf.EU!
How we evolved data pipeline at Celtra and what we learned along the wayGrega Kespret
Presented at Data Science Meetup on 4/12/2018.
In this talk, Grega Kespret (head of analytics group) will present Celtra’s data analytics pipeline and how it evolved through the years - sometimes forward, sometimes backward. On this journey, we became early adopter of different technologies: BigQuery, Vertica (pre-join projections), Spark (version 0.5), Databricks (beta users) and Snowflake (one of the first users). As the business grew and the product evolved, volume and complexity of data increased ten-fold, as has the number of users generating insights from this data. How come BigQuery did not scale? Why was choosing Vertica a mistake for our use case, and what have we learned from it? What requirements did we have for the analytics database, why did we have to abandon MySQL, and why we finally chose Snowflake? This talk will be heavily opinionated and will describe our experience and learnings - what worked for us and what didn't.
Researching an alternative to the MS SQL database - first of all in order to gain additional technological benefits, secondly moving towards an open source way of development.
The idea behind this presentation was to introduce PostgreSQL (ver. 9.4+) in a different manner than a conventional "Pros Vs. Cons" style, it is more likely to be a "Buzz Word" thesaurus (of course based on a deep research).
P.S. Since it's a presentation, there was no intention going over and covering all of the PostgreSQL features - most of the interesting parts.
Streaming ETL - from RDBMS to Dashboard with KSQLBjoern Rost
Apache Kafka is a massively scalable message queue that is being used at more and more places connecting more and more data sources. This presentation will introduce Kafka from the perspective of a mere mortal DBA and share the experience of (and challenges with) getting events from the database to Kafka using Kafka connect including poor-man’s CDC using flashback query and traditional logical replication tools. To demonstrate how and why this is a good idea, we will build an end-to-end data processing pipeline. We will discuss how to turn changes in database state into events and stream them into Apache Kafka. We will explore the basic concepts of streaming transformations using windows and KSQL before ingesting the transformed stream in a dashboard application.
VMworld 2013: Virtualizing Databases: Doing IT Right VMworld
VMworld 2013
Michael Corey, Ntirety, Inc
Jeff Szastak, VMware
Learn more about VMworld and register at http://www.vmworld.com/index.jspa?src=socmed-vmworld-slideshare
Presto: Fast SQL-on-Anything (including Delta Lake, Snowflake, Elasticsearch ...Databricks
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Comcast, GrubHub, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
Self-serve analytics journey at Celtra: Snowflake, Spark, and DatabricksGrega Kespret
Celtra provides a platform for streamlined ad creation and campaign management used by customers including Porsche, Taco Bell, and Fox to create, track, and analyze their digital display advertising. Celtra’s platform processes billions of ad events daily to give analysts fast and easy access to reports and ad hoc analytics. Celtra’s Grega Kešpret leads a technical dive into Celtra’s data-pipeline challenges and explains how it solved them by combining Snowflake’s cloud data warehouse with Spark to get the best of both.
Topics include:
- Why Celtra changed its pipeline, materializing session representations to eliminate the need to rerun its pipeline
- How and why it decided to use Snowflake rather than an alternative data warehouse or a home-grown custom solution
- How Snowflake complemented the existing Spark environment with the ability to store and analyze deeply nested data with full consistency
- How Snowflake + Spark enables production and ad hoc analytics on a single repository of data
This deck was the keynote speech delivered by Kevin Xu (GM of Global Strategy at Operations) and Shen Li (VP of Engineering at PingCAP) on TiDB architecture, tools and migration path, and TiDB Cloud fully-managed offering at Percona Live Europe 2018 in Frankfurt, Germany.
MySQL 8.0 is a big advancement over previous versions with a true data dictionary, invisible indexes, histograms, windowing functions, improved JSON support, CATS, and more
This DataStage internet Training will furnish you with the capability expected to work with the IBM DataStage. DataStage is an ETL device that uses a graphical documentation for the combination of information. This is the lead result of IBM in Business Intell
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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).
3. By The Numbers
▪10 months
▪30 releases (0.68 to 0.98)
▪42 contributors (57 total)
▪1761 commits (4583 total)
▪2406 files changed
▪198,680 insertions(+) 96,833 deletions(-)
4. Presto@Facebook
▪Scan PBs of data every day
▪Execute millions of queries each month
▪Process trillions rows a day
▪1000s of internal daily active users
5. New SQL Features
▪Structural types (array, map, row)
▪UNNEST (like Hive’s LATERAL VIEW)
▪Views
▪Aggregate window functions (rolling avg)
▪Tons of new functions (HLL, ML, etc)
▪Session properties
6. Hive
▪ORC, DWRF and Parquet
▪Real structural types
▪DATE type
▪Null partition keys
▪Improved partition pruning
8. Internal Changes
▪New query queueing system
▪Upgrade to Java 8
▪New ANTLR4 parser
▪New Bytecode compiler framework
▪New aggregation and window framework
▪Partition aware planner
▪IPv6 support (verified)
9. Optimizations
▪New ORC reader
▪Columnar reads, push down, and lazy
▪Reuse hash calculation across operators
▪Better work load balancing
▪Add “Big Query” support
▪Use partition metadata for simple queries
▪More parallel table writing
10. Optimizations
▪Wall and CPU efficiency improvements
▪50% for complex queries (joins, etc)
▪300% for simple queries (scan, filter, agg)
▪ORC Data
▪2-4x wall and CPU time speedup
▪4x+ speedup with lazy reads
▪30x+ speedup with predicate push down
11. 2014 Roadmap Checkin
Structural types
Create partition
Distributed joins
Huge joins
Task recovery
Work stealing
Native store
Security
Native ODBC
Plugin repository
Full pushdown
Optimizer plugins
15. Resource Management
▪New queueing system
▪Full global resource tracking
▪Per query limits -- not per task
▪Block queries until memory is available
16. Raptor
▪Initial use cases
▪Near real-time loads (every 5-15 minutes)
▪3 TB/day, 80B rows/day
▪5 second query over 1 day of data
▪Stores data in flash on worker nodes
▪Metadata is stored in MySQL
19. SELECT now() + INTERVAL ‘1’ YEAR
APPROXIMATE AT 95.0 CONFIDENCE
20. SELECT now() + INTERVAL ‘1’ YEAR
APPROXIMATE AT 95.0 CONFIDENCE
33.0
21. Resource Management
▪Automatic query scaling (no “Big Query”)
▪Better resource control
▪Resource take back
▪Bursting
▪Add and remove “extra” resources
24. Execution Engine
▪Result set caching support
▪Adaptive execution
▪Dictionary aware execution
▪Columnar structural types
▪Failure recovery
▪Draining (possibly work stealing)
25. SQL Features
▪Types with scale, precision, and length
▪varchar, char, varbinary, binary, decimal
▪Full DDL support
▪CREATE, ALTER, DROP
▪CUBE and ROLLUP
▪Scalar and correlated subqueries (EXISTS)