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.
ISO SQL:2016 introduced Row Pattern Matching: a feature to apply (limited) regular expressions on table rows and perform analysis on each match. As of writing, this feature is only supported by the Oracle Database 12c.
The final part of the SQL Tuning workshop focuses on applying the techniques discussed in the previous sections to help diagnose and correct a number of problematic SQL statements and shows how you can use SQL Plan Management or a SQL Patch to influence an execution plan.
Oracle SQL Developer Tips and Tricks: Data EditionJeff Smith
Originally presented at Oracle Code One 2019, this is a collection of techniques dedicated to making your work with data easier and more enjoyable in Oracle Database with SQL Developer.
ISO SQL:2016 introduced Row Pattern Matching: a feature to apply (limited) regular expressions on table rows and perform analysis on each match. As of writing, this feature is only supported by the Oracle Database 12c.
The final part of the SQL Tuning workshop focuses on applying the techniques discussed in the previous sections to help diagnose and correct a number of problematic SQL statements and shows how you can use SQL Plan Management or a SQL Patch to influence an execution plan.
Oracle SQL Developer Tips and Tricks: Data EditionJeff Smith
Originally presented at Oracle Code One 2019, this is a collection of techniques dedicated to making your work with data easier and more enjoyable in Oracle Database with SQL Developer.
The presentation helps to introduce the key aspects of the Oracle Optimizer and how you find out what it's up to and how you can influence its decisions.
After completing this lesson, you should be able to
do the following:
Describe each DML statement
Insert rows into a table
Update rows in a table
Delete rows from a table
Merge rows in a table
Control transactions
http://phpexecutor.com
Postgres expert, Bruce Momjian, as he discusses common table expressions (CTEs) and the ability to allow queries to be more imperative, allowing looping and processing hierarchical structures that are normally associated only with imperative languages.
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!
Powerful Spatial Features You Never Knew Existed in Oracle Spatial and Graph ...Jean Ihm
Dan Geringer - BIWA Summit 2018 presentation. Even expert users may not know some of the powerful functions available in Oracle Spatial and Graph, or how to optimize common spatial requirements. I often find myself working with customers that implement spatial requirements the way they had to with other spatial solutions, instead of the best way they can by leveraging powerful unique capabilities available in Oracle Spatial and Graph. Many times the reason is "I didn't know that existed". This session will cover how Oracle Spatial and Graph natively integrates with key Oracle Database features such as transparent data encryption (TDE), redaction, partitioning (all types), and also powerful nearest neighbor strategies, new spatial functions introduced in 12c, as well as an overview of spatial functions you never knew existed. Customer use cases and code examples will be included. This session is intended for a technical audience, but others will also gain useful insights on the powerful capabilities of Oracle Spatial and Graph.
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 Database performance tuning using oratopSandesh Rao
Oratop is a text-based user interface tool for monitoring basic database operations in real-time. This presentation will go into depth on how to use the tool and some example scenarios. It can be used for both RAC and single-instance databases and in combination with top to get a more holistic view of system performance and identify any bottlenecks.
Oracle Data Guard ensures high availability, disaster recovery and data protection for enterprise data. This enable production Oracle databases to survive disasters and data corruptions. Oracle 18c and 19c offers many new features it will bring many advantages to organization.
Part1 of SQL Tuning Workshop - Understanding the OptimizerMaria Colgan
Part 1 of a 5 part SQL Tuning workshop, This presentation covers the history of the Oracle Optimizer and explains the first thing the Optimizer does when it receives a SQL statements, which is to transform the SQL statement in order to open up additional access paths.
Troubleshooting Tips and Tricks for Database 19c - EMEA Tour Oct 2019Sandesh Rao
This session will focus on 19 troubleshooting tips and tricks for DBA's covering tools from the Oracle Autonomous Health Framework (AHF) like Trace file Analyzer (TFA) to collect , organize and analyze log data , Exachk and orachk to perform mass best practices analysis and automation , Cluster Health Advisor to debug node evictions and calibrate the framework , OSWatcher and its analysis engine , oratop for pinpointing performance issues and many others to make one feel like a rockstar DBA
Aggregating Data Using Group FunctionsSalman Memon
After completing this lesson, you should be able to
do the following:
Identify the available group functions
Describe the use of group functions
Group data using the GROUP BY clause
Include or exclude grouped rows by using the HAVING clause
http://phpexecutor.com
What Is SAS | SAS Tutorial For Beginners | SAS Training | SAS Programming | E...Edureka!
This Edureka "What Is SAS" tutorial will help you get started with SAS. This tutorial will also introduce you to Data Analytics and SAS Programming concepts. Below are the topics covered in this tutorial:
1. Data Analytics
2. Data Analytical Tools
3. Why SAS?
4. What Is SAS?
5. SAS Framework
6. SAS Programming Concepts
7. SAS Applications
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.
The presentation helps to introduce the key aspects of the Oracle Optimizer and how you find out what it's up to and how you can influence its decisions.
After completing this lesson, you should be able to
do the following:
Describe each DML statement
Insert rows into a table
Update rows in a table
Delete rows from a table
Merge rows in a table
Control transactions
http://phpexecutor.com
Postgres expert, Bruce Momjian, as he discusses common table expressions (CTEs) and the ability to allow queries to be more imperative, allowing looping and processing hierarchical structures that are normally associated only with imperative languages.
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!
Powerful Spatial Features You Never Knew Existed in Oracle Spatial and Graph ...Jean Ihm
Dan Geringer - BIWA Summit 2018 presentation. Even expert users may not know some of the powerful functions available in Oracle Spatial and Graph, or how to optimize common spatial requirements. I often find myself working with customers that implement spatial requirements the way they had to with other spatial solutions, instead of the best way they can by leveraging powerful unique capabilities available in Oracle Spatial and Graph. Many times the reason is "I didn't know that existed". This session will cover how Oracle Spatial and Graph natively integrates with key Oracle Database features such as transparent data encryption (TDE), redaction, partitioning (all types), and also powerful nearest neighbor strategies, new spatial functions introduced in 12c, as well as an overview of spatial functions you never knew existed. Customer use cases and code examples will be included. This session is intended for a technical audience, but others will also gain useful insights on the powerful capabilities of Oracle Spatial and Graph.
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 Database performance tuning using oratopSandesh Rao
Oratop is a text-based user interface tool for monitoring basic database operations in real-time. This presentation will go into depth on how to use the tool and some example scenarios. It can be used for both RAC and single-instance databases and in combination with top to get a more holistic view of system performance and identify any bottlenecks.
Oracle Data Guard ensures high availability, disaster recovery and data protection for enterprise data. This enable production Oracle databases to survive disasters and data corruptions. Oracle 18c and 19c offers many new features it will bring many advantages to organization.
Part1 of SQL Tuning Workshop - Understanding the OptimizerMaria Colgan
Part 1 of a 5 part SQL Tuning workshop, This presentation covers the history of the Oracle Optimizer and explains the first thing the Optimizer does when it receives a SQL statements, which is to transform the SQL statement in order to open up additional access paths.
Troubleshooting Tips and Tricks for Database 19c - EMEA Tour Oct 2019Sandesh Rao
This session will focus on 19 troubleshooting tips and tricks for DBA's covering tools from the Oracle Autonomous Health Framework (AHF) like Trace file Analyzer (TFA) to collect , organize and analyze log data , Exachk and orachk to perform mass best practices analysis and automation , Cluster Health Advisor to debug node evictions and calibrate the framework , OSWatcher and its analysis engine , oratop for pinpointing performance issues and many others to make one feel like a rockstar DBA
Aggregating Data Using Group FunctionsSalman Memon
After completing this lesson, you should be able to
do the following:
Identify the available group functions
Describe the use of group functions
Group data using the GROUP BY clause
Include or exclude grouped rows by using the HAVING clause
http://phpexecutor.com
What Is SAS | SAS Tutorial For Beginners | SAS Training | SAS Programming | E...Edureka!
This Edureka "What Is SAS" tutorial will help you get started with SAS. This tutorial will also introduce you to Data Analytics and SAS Programming concepts. Below are the topics covered in this tutorial:
1. Data Analytics
2. Data Analytical Tools
3. Why SAS?
4. What Is SAS?
5. SAS Framework
6. SAS Programming Concepts
7. SAS Applications
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
Build Deep Learning Applications Using Apache MXNet, Featuring Workday (AIM40...Amazon Web Services
The Apache MXNet deep learning framework is used for developing, training, and deploying diverse AI applications, including computer vision, speech recognition, and natural language processing at scale. In this session, learn how to get started with MXNet on the Amazon SageMaker machine learning platform. Hear from Workday about how they built computer vision and natural language processing (NLP) models using MXNet to automatically extract information from paper documents, such as expense receipts and populate data records. Workday also shares its experience using Sockeye, an MXNet toolkit for quickly prototyping sequence-to-sequence NLP models.
FINRA's Managed Data Lake: Next-Gen Analytics in the Cloud - ENT328 - re:Inve...Amazon Web Services
Financial Impact Regulatory Authority (FINRA)'s Technology Group has changed its customers' relationship with data by creating a managed data lake that enables discovery on petabytes of capital markets' data, while saving time and money over traditional analytics solutions. FINRA's managed data lake unlocks the value in its data to accelerate analytics and machine learning at scale. The data lake includes a centralized data catalog and separates storage from compute, allowing users to query from petabytes of data in seconds. Learn how FINRA uses Spot Instances and services such as Amazon S3, Amazon EMR, Amazon Redshift, and AWS Lambda to provide the right tool for the right job at each step in the data processing pipeline. All of this is done while meeting FINRA's security and compliance responsibilities as a financial regulator.
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
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
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)
A story about developing an application for an online store, persisting all the data as JSON.
Gives an overview of JSON functionality in Oracle Database 19c.
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
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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).
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
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found