The document discusses various SQL techniques for finding patterns in data, including identifying consecutive dates and dates that fall within the same week. It provides examples of using regular expressions, window functions, and Oracle Database 12c's MATCH_RECOGNIZE clause to analyze a sample running log dataset and determine consecutive runs, runs within the same week, and consecutive weeks with a minimum number of runs. The document compares different approaches like MATCH_RECOGNIZE versus the Tabibitosan method.
OPP2010 (Brussels) - Programming with XML in PL/SQL - Part 1Marco Gralike
The document provides an overview of XML programming in PL/SQL and Oracle XML DB. It discusses Oracle's XML capabilities and milestones from versions 9i to 11g. It highlights the various XML functions, operators, and packages available in Oracle for XML data handling and processing. It also provides examples of querying XML data stored in different sources using XMLTable and XQuery.
This document summarizes a presentation on Oracle RAC (Real Application Clusters) internals with a focus on Cache Fusion. The presentation covers:
1. An overview of Cache Fusion and how it allows data to be shared across instances to enable scalability.
2. Dynamic re-mastering which adjusts where data is mastered based on access patterns to reduce messaging.
3. Techniques for handling contention including partitioning, connection pools, and separating redo logs.
4. Benefits of combining Oracle Multitenant and RAC such as aligning PDBs to instances.
5. How Oracle In-Memory Column Store fully integrates with RAC including fault tolerance features.
This document discusses Oracle database performance tuning. It covers identifying common Oracle performance issues such as CPU bottlenecks, memory issues, and inefficient SQL statements. It also outlines the Oracle performance tuning method and tools like the Automatic Database Diagnostic Monitor (ADDM) and performance page in Oracle Enterprise Manager. These tools help administrators monitor performance, identify bottlenecks, implement ADDM recommendations, and tune SQL statements reactively when issues arise.
This is a recording of my Advanced Oracle Troubleshooting seminar preparation session - where I showed how I set up my command line environment and some of the main performance scripts I use!
DB Time, Average Active Sessions, and ASH Math - Oracle performance fundamentalsJohn Beresniewicz
RMOUG 2020 abstract:
This session will cover core concepts for Oracle performance analysis first introduced in Oracle 10g and forming the backbone of many features in the Diagnostic and Tuning packs. The presentation will cover the theoretical basis and meaning of these concepts, as well as illustrate how they are fundamental to many user-facing features in both the database itself and Enterprise Manager.
This document discusses techniques for optimizing SQL performance in Oracle databases. It covers topics like optimizing the optimizer itself through configuration changes and statistics collection, detecting poorly performing SQL, and methods for improving plans such as indexing, partitioning, hints and baselines. The goal is to maximize the optimizer's accuracy and ability to handle edge cases, while also knowing how to intervene when needed to capture fugitive SQL and ensure acceptable performance.
Any DBA from beginner to advanced level, who wants to fill in some gaps in his/her knowledge about Performance Tuning on an Oracle Database, will benefit from this workshop.
This document discusses execution plans in Oracle Database. It begins by explaining what an execution plan is and how it shows the steps needed to execute a SQL statement. It then covers how to generate an execution plan using EXPLAIN PLAN or querying V$SQL_PLAN. The document discusses what the optimizer considers a "good" plan in terms of cost and performance. It also explores key elements of an execution plan like cardinality, access paths, join methods, and join order.
OPP2010 (Brussels) - Programming with XML in PL/SQL - Part 1Marco Gralike
The document provides an overview of XML programming in PL/SQL and Oracle XML DB. It discusses Oracle's XML capabilities and milestones from versions 9i to 11g. It highlights the various XML functions, operators, and packages available in Oracle for XML data handling and processing. It also provides examples of querying XML data stored in different sources using XMLTable and XQuery.
This document summarizes a presentation on Oracle RAC (Real Application Clusters) internals with a focus on Cache Fusion. The presentation covers:
1. An overview of Cache Fusion and how it allows data to be shared across instances to enable scalability.
2. Dynamic re-mastering which adjusts where data is mastered based on access patterns to reduce messaging.
3. Techniques for handling contention including partitioning, connection pools, and separating redo logs.
4. Benefits of combining Oracle Multitenant and RAC such as aligning PDBs to instances.
5. How Oracle In-Memory Column Store fully integrates with RAC including fault tolerance features.
This document discusses Oracle database performance tuning. It covers identifying common Oracle performance issues such as CPU bottlenecks, memory issues, and inefficient SQL statements. It also outlines the Oracle performance tuning method and tools like the Automatic Database Diagnostic Monitor (ADDM) and performance page in Oracle Enterprise Manager. These tools help administrators monitor performance, identify bottlenecks, implement ADDM recommendations, and tune SQL statements reactively when issues arise.
This is a recording of my Advanced Oracle Troubleshooting seminar preparation session - where I showed how I set up my command line environment and some of the main performance scripts I use!
DB Time, Average Active Sessions, and ASH Math - Oracle performance fundamentalsJohn Beresniewicz
RMOUG 2020 abstract:
This session will cover core concepts for Oracle performance analysis first introduced in Oracle 10g and forming the backbone of many features in the Diagnostic and Tuning packs. The presentation will cover the theoretical basis and meaning of these concepts, as well as illustrate how they are fundamental to many user-facing features in both the database itself and Enterprise Manager.
This document discusses techniques for optimizing SQL performance in Oracle databases. It covers topics like optimizing the optimizer itself through configuration changes and statistics collection, detecting poorly performing SQL, and methods for improving plans such as indexing, partitioning, hints and baselines. The goal is to maximize the optimizer's accuracy and ability to handle edge cases, while also knowing how to intervene when needed to capture fugitive SQL and ensure acceptable performance.
Any DBA from beginner to advanced level, who wants to fill in some gaps in his/her knowledge about Performance Tuning on an Oracle Database, will benefit from this workshop.
This document discusses execution plans in Oracle Database. It begins by explaining what an execution plan is and how it shows the steps needed to execute a SQL statement. It then covers how to generate an execution plan using EXPLAIN PLAN or querying V$SQL_PLAN. The document discusses what the optimizer considers a "good" plan in terms of cost and performance. It also explores key elements of an execution plan like cardinality, access paths, join methods, and join order.
Harnessing the Power of Optimizer HintsMaria Colgan
The goal of the Oracle Optimizer is to examine all possible execution plans for a SQL statement and to pick the one with the lowest cost, which should be the most efficient. From time to time, it may become necessary to influence the plan the Optimizer chooses. The most powerful way to alter the plan chosen is via Optimizer hints. But knowing when and how to use Optimizer hints correctly is somewhat of a dark art. This session explains in detail how Optimizer hints are interpreted, when they should be used, and why they sometimes appear to be ignored.
Oracle SQL tuning involves optimizing SQL statements for better performance. Key aspects of SQL tuning include identifying SQL statements with high resource consumption or response times using tools like ADDM, AWR, and V$SQL. Statements can then be tuned by gathering accurate optimizer statistics, adjusting the execution plan using hints, rewriting the SQL, or changing indexes and tables. Tuning is done at both the design and execution stages.
The document is an introduction to the MySQL 8.0 optimizer guide. It includes a safe harbor statement noting that the guide outlines Oracle's general product direction but not commitments. The agenda lists 25 topics to be covered related to query optimization, diagnostic commands, examples from the "World Schema" sample database, and a companion website with more details.
The document discusses Oracle database performance tuning. It covers reactive and proactive performance tuning, the top-down tuning methodology, common types of performance issues, and metrics for measuring performance such as response time and throughput. It also compares online transaction processing (OLTP) systems and data warehouses (DW), and describes different architectures for integrating OLTP and DW systems.
Understanding my database through SQL*Plus using the free tool eDB360Carlos Sierra
This session introduces eDB360 - a free tool that is executed from SQL*Plus and generates a set of reports providing a 360-degree view of an Oracle database; all without installing anything on the database.
If using Oracle Enterprise Manager (OEM) is off-limits for you or your team, and you can only access the database thorough a SQL*Plus connection with no direct access to the database server, then this tool is a perfect fit to provide you with a broad overview of the database configuration, performance, top SQL and much more. You only need a SQL*Plus account with read access to the data dictionary, and common Oracle licenses like the Diagnostics or the Tuning Pack.
Typical uses of this eDB360 tool include: databases health-checks, performance assessments, pre or post upgrade verifications, snapshots of the environment for later use, compare between two similar environments, documenting the state of a database when taking ownership of it, etc.
Once you learn how to use eDB360 and get to appreciate its value, you may want to execute this tool on all your databases on a regular basis, so you can keep track of things for long periods of time. This tool is becoming part of a large collection of goodies many DBAs use today.
During this session you will learn the basics about the free eDB360 tool, plus some cool tricks. The target audience is: DBAs, developers and consultants (some managers could also benefit).
Is it easier to add functional programming features to a query language, or to add query capabilities to a functional language? In Morel, we have done the latter.
Functional and query languages have much in common, and yet much to learn from each other. Functional languages have a rich type system that includes polymorphism and functions-as-values and Turing-complete expressiveness; query languages have optimization techniques that can make programs several orders of magnitude faster, and runtimes that can use thousands of nodes to execute queries over terabytes of data.
Morel is an implementation of Standard ML on the JVM, with language extensions to allow relational expressions. Its compiler can translate programs to relational algebra and, via Apache Calcite’s query optimizer, run those programs on relational backends.
In this talk, we describe the principles that drove Morel’s design, the problems that we had to solve in order to implement a hybrid functional/relational language, and how Morel can be applied to implement data-intensive systems.
(A talk given by Julian Hyde at Strange Loop 2021, St. Louis, MO, on October 1st, 2021.)
The MySQL Query Optimizer Explained Through Optimizer Traceoysteing
The document discusses the MySQL query optimizer. It begins by explaining how the optimizer works, including analyzing statistics, determining optimal join orders and access methods. It then describes how the optimizer trace can provide insight into why a particular execution plan was selected. The remainder of the document provides details on the various phases the optimizer goes through, including logical transformations, cost-based optimizations like range analysis and join order selection.
The document discusses how queries work in sharded MongoDB environments. It explains that MongoDB collections are partitioned into chunks based on a shard key, and each chunk is assigned to a particular shard. When a query is executed, the mongos process routes it to the correct shard(s) based on the shard key range in the query. Queries involving only the shard key are efficient, targeting specific shards. Queries on non-shard keys require scattering and gathering across all shards, but secondary indexes can help efficiency on each shard.
Understanding SQL Trace, TKPROF and Execution Plan for beginnersCarlos Sierra
The three fundamental steps of SQL Tuning are: 1) Diagnostics Collection; 2) Root Cause Analysis (RCA); and 3) Remediation. This introductory session on SQL Tuning is for novice DBAs and Developers that are required to investigate a piece of an application performing poorly.
On this session participants will learn about producing a SQL Trace then a summary TKPROF report. A sample TKPROF is navigated with the audience, where the trivial and the no so trivial is exposed and explain. Execution Plans are also navigated and explained, so participants can later untangle complex Execution Plans and start diagnosing SQL performing badly.
This session encourages participants to ask all kind of questions that could be potential road-blocks for deeper understanding of how to address a SQL performing poorly.
- The document discusses advanced techniques for optimizing MySQL queries, including topics like temporary tables, file sorting, order optimizations, and calculated fields.
- It provides examples of using indexes and index optimizations, explaining concepts like index types, index usage, key lengths, and covering indexes.
- One example shows how to optimize a query involving a calculated year() expression by rewriting the query to use a range on the date field instead.
The document describes the steps to configure Oracle sharding in an Oracle 12c environment. It includes installing Oracle software on shardcat, shard1, and shard2 nodes, creating an SCAT database, installing the GSM software, configuring the shard catalog, registering the shard nodes, creating a shard group and adding shards, deploying the shards to create databases on shard1 and shard2, verifying the shard configuration, creating a global service, and creating a sample schema and shard table to verify distribution across shards.
Advanced PLSQL Optimizing for Better PerformanceZohar Elkayam
A Presentation from Oracle Week 2015 in Israel
Agenda:
• Developing PL/SQL:
o Composite Data Types: Records, Collections and Table type
o Advanced Cursors: Ref cursor, Cursor function, Cursor subquery in PL/SQL
o Bulk Binding
o Dynamic SQL – SQL Injection
o Tracing PL/SQL Execution
o Design patterns for PL/SQL: Autonomous Transactions, Invoker and Definer rights, serially_reusable code
o Triggers Improvements
• Compiling PL/SQL:
o PL/SQL Fine-Grain Dependency Management
o PLSQL_OPTIMIZE_LEVEL parameter
o PL/SQL Compile-Time Warnings and Using DBMS_WARNING package
• Tuning PL/SQL:
o Handling Packages in Memory
o Global Temporary Tables
o PL/SQL Function Result Cache and pitfalls
• Oracle Database 12c PL/SQL new features: What is new in Oracle 12c
o Language Usability Enhancements
o New Limitations
• Additional useful features, Tips and Tricks for better performance
Exploring Oracle Database Performance Tuning Best Practices for DBAs and Deve...Aaron Shilo
The document provides an overview of Oracle database performance tuning best practices for DBAs and developers. It discusses the connection between SQL tuning and instance tuning, and how tuning both the database and SQL statements is important. It also covers the connection between the database and operating system, how features like data integrity and zero downtime updates are important. The presentation agenda includes topics like identifying bottlenecks, benchmarking, optimization techniques, the cost-based optimizer, indexes, and more.
Oracle Database Performance Tuning Advanced Features and Best Practices for DBAsZohar Elkayam
Oracle Week 2017 slides.
Agenda:
Basics: How and What To Tune?
Using the Automatic Workload Repository (AWR)
Using AWR-Based Tools: ASH, ADDM
Real-Time Database Operation Monitoring (12c)
Identifying Problem SQL Statements
Using SQL Performance Analyzer
Tuning Memory (SGA and PGA)
Parallel Execution and Compression
Oracle Database 12c Performance New Features
The document discusses Oracle Real Application Clusters (Oracle RAC) and how it provides high availability and scalability for Oracle Database workloads. Oracle RAC uses a shared-nothing architecture with multiple independent database instances managing a shared database, and it leverages a cluster interconnect for communication between the nodes. Key features of Oracle RAC discussed include dynamic resource management for improved performance, hang detection and resolution capabilities, and service-oriented buffer cache access to optimize data access based on service location.
Session aims at introducing less familiar audience to the Oracle database statistics concept, why statistics are necessary and how the Oracle Cost-Based Optimizer uses them
What Is SAS | SAS Tutorial For Beginners | SAS Training | SAS Programming | E...Edureka!
The document discusses SAS (Statistical Analytics System), a software for data management, analytics and visualization. It provides an overview of SAS framework, programming and applications. SAS allows users to access, manage and analyze data, and then present results. It discusses key SAS concepts like data sets, variables, formats and linear regression modeling using SAS procedures. Common applications of SAS mentioned are in domains like stock prediction, drug discovery, fraud detection and workflow optimization.
5th in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
PGQL: A Query Language for Graphs
Learn how to query graphs using PGQL, an expressive and intuitive graph query language that's a lot like SQL. With PGQL, it's easy to get going writing graph analysis queries to the database in a very short time. Albert and Oskar show what you can do with PGQL, and how to write and execute PGQL code.
Harnessing the Power of Optimizer HintsMaria Colgan
The goal of the Oracle Optimizer is to examine all possible execution plans for a SQL statement and to pick the one with the lowest cost, which should be the most efficient. From time to time, it may become necessary to influence the plan the Optimizer chooses. The most powerful way to alter the plan chosen is via Optimizer hints. But knowing when and how to use Optimizer hints correctly is somewhat of a dark art. This session explains in detail how Optimizer hints are interpreted, when they should be used, and why they sometimes appear to be ignored.
Oracle SQL tuning involves optimizing SQL statements for better performance. Key aspects of SQL tuning include identifying SQL statements with high resource consumption or response times using tools like ADDM, AWR, and V$SQL. Statements can then be tuned by gathering accurate optimizer statistics, adjusting the execution plan using hints, rewriting the SQL, or changing indexes and tables. Tuning is done at both the design and execution stages.
The document is an introduction to the MySQL 8.0 optimizer guide. It includes a safe harbor statement noting that the guide outlines Oracle's general product direction but not commitments. The agenda lists 25 topics to be covered related to query optimization, diagnostic commands, examples from the "World Schema" sample database, and a companion website with more details.
The document discusses Oracle database performance tuning. It covers reactive and proactive performance tuning, the top-down tuning methodology, common types of performance issues, and metrics for measuring performance such as response time and throughput. It also compares online transaction processing (OLTP) systems and data warehouses (DW), and describes different architectures for integrating OLTP and DW systems.
Understanding my database through SQL*Plus using the free tool eDB360Carlos Sierra
This session introduces eDB360 - a free tool that is executed from SQL*Plus and generates a set of reports providing a 360-degree view of an Oracle database; all without installing anything on the database.
If using Oracle Enterprise Manager (OEM) is off-limits for you or your team, and you can only access the database thorough a SQL*Plus connection with no direct access to the database server, then this tool is a perfect fit to provide you with a broad overview of the database configuration, performance, top SQL and much more. You only need a SQL*Plus account with read access to the data dictionary, and common Oracle licenses like the Diagnostics or the Tuning Pack.
Typical uses of this eDB360 tool include: databases health-checks, performance assessments, pre or post upgrade verifications, snapshots of the environment for later use, compare between two similar environments, documenting the state of a database when taking ownership of it, etc.
Once you learn how to use eDB360 and get to appreciate its value, you may want to execute this tool on all your databases on a regular basis, so you can keep track of things for long periods of time. This tool is becoming part of a large collection of goodies many DBAs use today.
During this session you will learn the basics about the free eDB360 tool, plus some cool tricks. The target audience is: DBAs, developers and consultants (some managers could also benefit).
Is it easier to add functional programming features to a query language, or to add query capabilities to a functional language? In Morel, we have done the latter.
Functional and query languages have much in common, and yet much to learn from each other. Functional languages have a rich type system that includes polymorphism and functions-as-values and Turing-complete expressiveness; query languages have optimization techniques that can make programs several orders of magnitude faster, and runtimes that can use thousands of nodes to execute queries over terabytes of data.
Morel is an implementation of Standard ML on the JVM, with language extensions to allow relational expressions. Its compiler can translate programs to relational algebra and, via Apache Calcite’s query optimizer, run those programs on relational backends.
In this talk, we describe the principles that drove Morel’s design, the problems that we had to solve in order to implement a hybrid functional/relational language, and how Morel can be applied to implement data-intensive systems.
(A talk given by Julian Hyde at Strange Loop 2021, St. Louis, MO, on October 1st, 2021.)
The MySQL Query Optimizer Explained Through Optimizer Traceoysteing
The document discusses the MySQL query optimizer. It begins by explaining how the optimizer works, including analyzing statistics, determining optimal join orders and access methods. It then describes how the optimizer trace can provide insight into why a particular execution plan was selected. The remainder of the document provides details on the various phases the optimizer goes through, including logical transformations, cost-based optimizations like range analysis and join order selection.
The document discusses how queries work in sharded MongoDB environments. It explains that MongoDB collections are partitioned into chunks based on a shard key, and each chunk is assigned to a particular shard. When a query is executed, the mongos process routes it to the correct shard(s) based on the shard key range in the query. Queries involving only the shard key are efficient, targeting specific shards. Queries on non-shard keys require scattering and gathering across all shards, but secondary indexes can help efficiency on each shard.
Understanding SQL Trace, TKPROF and Execution Plan for beginnersCarlos Sierra
The three fundamental steps of SQL Tuning are: 1) Diagnostics Collection; 2) Root Cause Analysis (RCA); and 3) Remediation. This introductory session on SQL Tuning is for novice DBAs and Developers that are required to investigate a piece of an application performing poorly.
On this session participants will learn about producing a SQL Trace then a summary TKPROF report. A sample TKPROF is navigated with the audience, where the trivial and the no so trivial is exposed and explain. Execution Plans are also navigated and explained, so participants can later untangle complex Execution Plans and start diagnosing SQL performing badly.
This session encourages participants to ask all kind of questions that could be potential road-blocks for deeper understanding of how to address a SQL performing poorly.
- The document discusses advanced techniques for optimizing MySQL queries, including topics like temporary tables, file sorting, order optimizations, and calculated fields.
- It provides examples of using indexes and index optimizations, explaining concepts like index types, index usage, key lengths, and covering indexes.
- One example shows how to optimize a query involving a calculated year() expression by rewriting the query to use a range on the date field instead.
The document describes the steps to configure Oracle sharding in an Oracle 12c environment. It includes installing Oracle software on shardcat, shard1, and shard2 nodes, creating an SCAT database, installing the GSM software, configuring the shard catalog, registering the shard nodes, creating a shard group and adding shards, deploying the shards to create databases on shard1 and shard2, verifying the shard configuration, creating a global service, and creating a sample schema and shard table to verify distribution across shards.
Advanced PLSQL Optimizing for Better PerformanceZohar Elkayam
A Presentation from Oracle Week 2015 in Israel
Agenda:
• Developing PL/SQL:
o Composite Data Types: Records, Collections and Table type
o Advanced Cursors: Ref cursor, Cursor function, Cursor subquery in PL/SQL
o Bulk Binding
o Dynamic SQL – SQL Injection
o Tracing PL/SQL Execution
o Design patterns for PL/SQL: Autonomous Transactions, Invoker and Definer rights, serially_reusable code
o Triggers Improvements
• Compiling PL/SQL:
o PL/SQL Fine-Grain Dependency Management
o PLSQL_OPTIMIZE_LEVEL parameter
o PL/SQL Compile-Time Warnings and Using DBMS_WARNING package
• Tuning PL/SQL:
o Handling Packages in Memory
o Global Temporary Tables
o PL/SQL Function Result Cache and pitfalls
• Oracle Database 12c PL/SQL new features: What is new in Oracle 12c
o Language Usability Enhancements
o New Limitations
• Additional useful features, Tips and Tricks for better performance
Exploring Oracle Database Performance Tuning Best Practices for DBAs and Deve...Aaron Shilo
The document provides an overview of Oracle database performance tuning best practices for DBAs and developers. It discusses the connection between SQL tuning and instance tuning, and how tuning both the database and SQL statements is important. It also covers the connection between the database and operating system, how features like data integrity and zero downtime updates are important. The presentation agenda includes topics like identifying bottlenecks, benchmarking, optimization techniques, the cost-based optimizer, indexes, and more.
Oracle Database Performance Tuning Advanced Features and Best Practices for DBAsZohar Elkayam
Oracle Week 2017 slides.
Agenda:
Basics: How and What To Tune?
Using the Automatic Workload Repository (AWR)
Using AWR-Based Tools: ASH, ADDM
Real-Time Database Operation Monitoring (12c)
Identifying Problem SQL Statements
Using SQL Performance Analyzer
Tuning Memory (SGA and PGA)
Parallel Execution and Compression
Oracle Database 12c Performance New Features
The document discusses Oracle Real Application Clusters (Oracle RAC) and how it provides high availability and scalability for Oracle Database workloads. Oracle RAC uses a shared-nothing architecture with multiple independent database instances managing a shared database, and it leverages a cluster interconnect for communication between the nodes. Key features of Oracle RAC discussed include dynamic resource management for improved performance, hang detection and resolution capabilities, and service-oriented buffer cache access to optimize data access based on service location.
Session aims at introducing less familiar audience to the Oracle database statistics concept, why statistics are necessary and how the Oracle Cost-Based Optimizer uses them
What Is SAS | SAS Tutorial For Beginners | SAS Training | SAS Programming | E...Edureka!
The document discusses SAS (Statistical Analytics System), a software for data management, analytics and visualization. It provides an overview of SAS framework, programming and applications. SAS allows users to access, manage and analyze data, and then present results. It discusses key SAS concepts like data sets, variables, formats and linear regression modeling using SAS procedures. Common applications of SAS mentioned are in domains like stock prediction, drug discovery, fraud detection and workflow optimization.
5th in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
PGQL: A Query Language for Graphs
Learn how to query graphs using PGQL, an expressive and intuitive graph query language that's a lot like SQL. With PGQL, it's easy to get going writing graph analysis queries to the database in a very short time. Albert and Oskar show what you can do with PGQL, and how to write and execute PGQL code.
20190615 hkos-mysql-troubleshootingandperformancev2Ivan Ma
MySQL Troubleshooting in Hong Kong Open Source Conference 2019 - how to use sys.diagnostics(...) and using the dimitri (http://dimitrik.free.fr/) Tools for performance analysis.
Oracle to Amazon Aurora Migration, Step by Step - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Learn about the challenges of migrating between heterogeneous databases
- Highlight differences between Oracle and PostgreSQL database engines
- Look at detailed examples of migrating features to Amazon Aurora
The document provides a summary of activities within the Java Community Process (JCP) in 2018. Some key points:
- JCP membership decreased 11% to 1101 members, with most individual and corporate members located in North America and Europe.
- 16 active JSRs addressed Java SE, EE, and embedded/desktop platforms. Oracle led the most JSRs both currently and overall.
- 170 expert group members from 54 organizations contributed to the 16 active JSRs. Oracle, IBM, and Red Hat had the most representatives.
- Voter participation in annual JCP Executive Committee elections was 40% for full members and 44% for associates.
Build Deep Learning Applications Using Apache MXNet, Featuring Workday (AIM40...Amazon Web Services
The document discusses using machine learning for information extraction from enterprise documents. It describes using MXNet and Apache SageMaker for building and deploying models. It discusses various algorithms and techniques used for problems like document scanning, text recognition and understanding.
FINRA's Managed Data Lake: Next-Gen Analytics in the Cloud - ENT328 - re:Inve...Amazon Web Services
FINRA faced challenges with their on-premises data infrastructure, including difficulty tracking data, limited scalability, and high costs. They migrated to a managed data lake on AWS to address these issues. This provided centralized data management with a catalog, separation of storage and compute, encryption, and cost optimization. It enabled faster analytics through Presto querying, machine learning model development, and reduced TCO by 30% compared to their on-premises environment. Lessons learned included embracing disruption, automating infrastructure, and treating infrastructure as code. FINRA is exploring additional AWS services like Athena, Lambda, and Step Functions to continue improving their analytics capabilities.
Azure HDlnsight에서 R 및 Spark를 이용하여 확장 가능한 머신러닝OSS On Azure
'애저, 오픈소스의 날개를 달다 웨비나 2'_20171214
Microsoft 한석진 부장, 락플레이스 최덕순 부장
- Azure HDlnsight에서 R 및 Spark를 이용하여 확장 가능한 머신러닝 소개
- 문의 락플레이스 MS사업본부(msbiz@rockplace.co.kr)
ReactJS Tutorial For Beginners | ReactJS Redux Training For Beginners | React...Edureka!
This Edureka ReactJS Tutorial For Beginners will help you in understanding the fundamentals of ReactJS and help you in building a strong foundation in React framework. Below are the topics covered in this tutorial:
1. Why ReactJS?
2. What Is ReactJS?
3. Advantages Of ReactJS
4. ReactJS Installation and Program
5. ReactJS Fundamentals
Build Deep Learning Applications Using Apache MXNet - Featuring Chick-fil-A (...Amazon Web Services
The Apache MXNet deep learning framework is used for developing, training, and deploying diverse AI applications, including computer vision, speech recognition, natural language processing, and more at scale. In this session, learn how to get started with Apache MXNet on the Amazon SageMaker machine learning platform. Chick-fil-A share how they got started with MXNet on Amazon SageMaker to measure waffle fry freshness and how they leverage AWS services to improve the Chick-fil-A guest experience.
Data preparation and transformation - Spin your straw into gold - Tel Aviv Su...Amazon Web Services
Data preparation is always a challenge. Why care about infrastructure?
Come learn how to deploy your Spark jobs in minutes using our managed services, EMR & Glue and focus on your business needs.
Data Warehousing and Data Lake Analytics, Together - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Learn how to discover and prepare your data lake for analytics
- See how you can query across your data warehouse and data lake without moving data
- Understand use cases that give you freedom to store data where you want and analyze it when you need it
The document discusses strategies for migrating databases to the cloud. It begins by outlining objectives and factors that contribute to successful database migration projects. It then covers various migration options like rehosting, replatforming, or rearchitecting databases. The document also discusses tools like the Schema Conversion Tool (SCT) and Database Migration Service (DMS) that can assist with assessment, conversion between database engines, and migrating data between on-premises and cloud databases. The overall process involves planning, assessment, executing the migration in phases, and testing.
SageMaker Algorithms Infinitely Scalable Machine LearningAmazon Web Services
by Nick Brandaleone, Solutions Architect, AWS
Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models, at any scale. Amazon SageMaker provides high-performance, machine learning algorithms optimized for speed, scale, and accuracy, to perform training on petabyte-scale data sets. This session will introduce you to the collection of distributed streaming ML algorithms that come with Amazon SageMaker. You will learn about the difference between streaming and batch ML algorithms, and how SageMaker has been architected to run these algorithms at scale. We will demo Neural Topic Modeling of text documents using a sample SageMaker Notebook, which will be made available to attendees.
Amazon SageMaker Algorithms: Machine Learning Week San FranciscoAmazon Web Services
Machine Learning Week at the San Francisco Loft: Amazon SageMaker Algorithm
Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models, at any scale. Amazon SageMaker provides built-in algorithms that are capable of scaling to immense data set sizes. In this example we'll discuss what makes SageMaker algorithms different and how you can leverage them for your largest, most complex, machine learning projects.
Speaker: David Arpin - AI Platform Selections Leader, AI Platforms
#dbhouseparty - Real World Problem Solving with SQLTammy Bednar
This document discusses using SQL to solve real-world problems. It introduces the presenters Kim Berg Hansen and Chris Saxon. The document then provides examples of using analytic functions in SQL to calculate sales forecasts, remaining stock, and time to zero stock. It also demonstrates using analytic functions to find optimal stock picking routes and count children in a hierarchical dataset. The document emphasizes that SQL can be used to efficiently solve complex business problems.
The JCP program activities for 2019 are summarized. There were 14 active JSRs during the year, with 2 new JSRs started and 3 completed. Total JCP membership increased slightly to 1,131 members. Voter participation in the annual EC election was 30%. A total of 135 experts participated in the expert groups for the active JSRs, representing 37 organizations. The 2019 JCP award winners were recognized for their work on JSR 385.
Top 10 SQL Performance tips & tricks for Java Developersgvenzl
This slide deck contains some of the most common database performance tips and tricks that developers can use to tune their applications or systems. It also highlights some anti-patterns and shows the impact of these anti-patterns in regard to performance.
This slide deck does not aim to be a complete list of all possibilities and techniques to achieve better performance but just highlights some very commonly seen mistakes and how to avoid them.
Melbourne Groundbreakers Tour - Upgrading without riskConnor McDonald
The 12c optimizer has a vast array of improvements, but of course, functionality changes means that your SQL plans might also change when you upgrade. This slidedeck covers what has changed, and how to ensure better more stable performance when you upgrade.
The 12c optimizer has a vast array of improvements, but of course, functionality changes means that your SQL plans might also change when you upgrade. This slidedeck covers what has changed, and how to ensure better more stable performance when you upgrade.
Similar to How to Find Patterns in Your Data with SQL (20)
Game of Fraud Detection with SQL and Machine LearningChris Saxon
This document summarizes a presentation about using SQL and machine learning to detect fraud. It discusses using SQL rules to initially flag fraudulent transactions based on attributes like transaction amount and sender/receiver. This achieved an error rate of 1% but still lost money. Machine learning was then applied by splitting data into train and test, building a naive Bayes model, and computing metrics like the confusion matrix. Combining SQL rules and ML results improved performance over either approach individually, lowering the error rate. The document emphasizes that the real world requires balancing false positives versus negatives based on business impacts. It promotes using both deterministic and probabilistic approaches together.
This document discusses storing product and order data as JSON in a database to support an agile development process. It describes creating tables with JSON columns to store this data, and using JSON functions like JSON_VALUE and JSON_TABLE to query and transform the JSON data. Examples are provided of indexing JSON columns for performance and updating product JSON to include unit costs by joining external data. The goal is to enable flexible and rapid evolution of the application through storing data in JSON.
Added in Oracle Database 18c, Polymorphic Table Functions (PTFs) allow you to change the shape of a result set at runtime. So you can add or remove columns from your results based on input parameters.
This presentation gives an overview of the why & how of PTFs.
Using Edition-Based Redefinition for Zero Downtime PL/SQL ChangesChris Saxon
An introduction to edition-based redefinition, a technology which enables zero-downtime application releases for Oracle Database. Discusses the challenges with deploying PL/SQL code changes, and shows how EBR solves these issues.
Why Isn't My Query Using an Index? An Introduction to SQL PerformanceChris Saxon
An introduction to the factors that affect whether or not the optimizer will choose an index to execute a query.
Explains the clustering factor. What this is, why it matters, and how it affects query performance. It also covers techniques you can use to change the clustering factor for a table.
18(ish) Things You'll Love About Oracle Database 18cChris Saxon
An overview of the latest SQL & PL/SQL features in Oracle Database 18c, including:
- Polymorphic Table Functions
- Inline External Tables
- JSON improvements
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Discover the cutting-edge telemetry solution implemented for Alan Wake 2 by Remedy Entertainment in collaboration with AWS. This comprehensive presentation dives into our objectives, detailing how we utilized advanced analytics to drive gameplay improvements and player engagement.
Key highlights include:
Primary Goals: Implementing gameplay and technical telemetry to capture detailed player behavior and game performance data, fostering data-driven decision-making.
Tech Stack: Leveraging AWS services such as EKS for hosting, WAF for security, Karpenter for instance optimization, S3 for data storage, and OpenTelemetry Collector for data collection. EventBridge and Lambda were used for data compression, while Glue ETL and Athena facilitated data transformation and preparation.
Data Utilization: Transforming raw data into actionable insights with technologies like Glue ETL (PySpark scripts), Glue Crawler, and Athena, culminating in detailed visualizations with Tableau.
Achievements: Successfully managing 700 million to 1 billion events per month at a cost-effective rate, with significant savings compared to commercial solutions. This approach has enabled simplified scaling and substantial improvements in game design, reducing player churn through targeted adjustments.
Community Engagement: Enhanced ability to engage with player communities by leveraging precise data insights, despite having a small community management team.
This presentation is an invaluable resource for professionals in game development, data analytics, and cloud computing, offering insights into how telemetry and analytics can revolutionize player experience and game performance optimization.
06-18-2024-Princeton Meetup-Introduction to MilvusTimothy Spann
06-18-2024-Princeton Meetup-Introduction to Milvus
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://github.com/milvus-io/milvus
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/142-17June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
Expand LLMs' knowledge by incorporating external data sources into LLMs and your AI applications.