Optimising Queries - Series 3 Distinguishing among query types
=> Point
=> Multipoint
=> Range
=> Prefix match
=> Extremal
=> Ordering
=> Grouping
=> Join
by DR. SUBRAMANI PARAMASIVAM
PostgreSQL - It's kind've a nifty databaseBarry Jones
This presentation was given to a company that makes software for churches that is considering a migration from SQL Server to PostgreSQL. It was designed to give a broad overview of features in PostgreSQL with an emphasis on full-text search, various datatypes like hstore, array, xml, json as well as custom datatypes, TOAST compression and a taste of other interesting features worth following up on.
Technical marketers are in high demand and low supply. Being able to dive into data on your own, with no help from engineering, makes you a much better marketer.
This is why SQL is so powerful - it allows you to see any data you want about anything your customers do. Knowing how to use SQL is literally a marketing superpower.
In this SQL tutorial specifically for marketers, I've pulled together SQL query basics that any marketer or data analyst will need to dig into their customer analytics. This course is the best resource for marketers, growth hackers and product managers who want to get more technical and learn SQL. It's what I wish existed when I was going through tutorial after tutorial, sifting through lots of information that didn't apply to me and trying to learn on my own.
SQL is simple enough that - just by learning a few concepts I cover above - you'll be able to use it for any kind of data analysis, cohort analysis or campaign breakdown.
Want more information? Check out resources on my blog - http://justinmares.com/sql
PostgreSQL - It's kind've a nifty databaseBarry Jones
This presentation was given to a company that makes software for churches that is considering a migration from SQL Server to PostgreSQL. It was designed to give a broad overview of features in PostgreSQL with an emphasis on full-text search, various datatypes like hstore, array, xml, json as well as custom datatypes, TOAST compression and a taste of other interesting features worth following up on.
Technical marketers are in high demand and low supply. Being able to dive into data on your own, with no help from engineering, makes you a much better marketer.
This is why SQL is so powerful - it allows you to see any data you want about anything your customers do. Knowing how to use SQL is literally a marketing superpower.
In this SQL tutorial specifically for marketers, I've pulled together SQL query basics that any marketer or data analyst will need to dig into their customer analytics. This course is the best resource for marketers, growth hackers and product managers who want to get more technical and learn SQL. It's what I wish existed when I was going through tutorial after tutorial, sifting through lots of information that didn't apply to me and trying to learn on my own.
SQL is simple enough that - just by learning a few concepts I cover above - you'll be able to use it for any kind of data analysis, cohort analysis or campaign breakdown.
Want more information? Check out resources on my blog - http://justinmares.com/sql
Scalable Data Models with ElasticsearchBeyondTrees
At bol.com, a leading ecommerce platform in The Netherlands, we have done extensive research into what it would take to use ElasticSearch as the main search provider. We will explain the specific challenges and requirements of running an Elasticsearch cluster at bol.com-scale, and show how we have used generated data to do performance and scalability tests on different ways to model a hierarchical data model into Elasticsearch. We will describe the benefits and drawbacks of the different data model options, and their consequences for the design of the index and search applications.
Mariia Havrylovych "Active learning and weak supervision in NLP projects"Fwdays
Successful artificial intelligence solutions always require a massive amount of high-quality labeled data. In most cases, we don’t have a large and qualitative labeled set together. Weak supervision and active learning tools may help you optimize the labeling process and address the shortage of data labels.
First, we will review how active learning can significantly reduce the amount of labeled data for training with classic approaches. We will show how active learning methods can be customized for a specific (NLP) task by using text embedding.
With weak supervision, we will see how using simple rules gets a big train dataset automatically and high model performance without manual labeling at all.
In the end, we will combine active learning and weak supervision by taking advantage of both techniques and achieving the best metrics.
ETL Testing Training | ETL Testing Training In BangaloreVyshnavi Reddy
Tek Classes is one of the best etl testing training institute in bangalore.ETL Testing Training is designed for beginners to advanced Professionals. The course includes the following topics – Introduction to ETL Testing, OLTP vs. OLAP, Introduction to RDBMS, Database Testing Vs Data Warehouse Testing, Data Warehouse Workflow and Case Study, Data Checks using SQL and Scope of BI testing.
http://tekclasses.com/course/etl-testing-training/
How Clean is your Database? Data Scrubbing for all Skill SetsChad Petrovay
With staff working from home, many institutions are prioritizing data quality projects. Join Chad Petrovay, TMS Administrator at The Morgan Library & Museum, as he shares his deep knowledge of data scrubbing. Power users, system administrators, and SQL experts will learn how to correct and monitor data quality, and are introduced to new low-cost/free tools.
Why MongoDB over other Databases - HabilelabsHabilelabs
MongoDB is the faster-growing database. It is an open-source document and leading NoSQL database with the scalability and flexibility that you want with the querying and indexing that you need. In this Document, I presented why to choose MongoDB is over another database.
MYSQL Query Anti-Patterns That Can Be Moved to SphinxPythian
PalominoDB European Team lead, Vladimir Fedorkov will be discussing how to handle query bottlenecks that can result from increases in dataset and traffic
Crucial Tips to Improve MySQL Database Performance.pptxTosska Technology
A lot of database professionals have learned significantly about the issues arising in software projects that needed a Database Management System for storing information in the backend.
These are the slides from my presentation on Running R in the Database using Oracle R Enterprise. The second half of the presentation is a live demo of using the Oracle R Enterprise. Unfortunately the demo is not listed in these slides
There are many data modeling and database design terms and jargon that uses the word "key." Do you know the difference between a surrogate key and a primary key? A super key and a candidate key? Could you explain them to a technical audience? A business user or an auditor?
In this presentation, Karen Lopez covers the concepts of primary keys, foreign keys, candidate key, surrogate keys, and more.
Using Compass to Diagnose Performance Problems MongoDB
Speaker: Brian Blevins, Technical Services Engineer, MongoDB
Level: 200 (Intermediate)
Track: Performance
Since the performance of your application drives engagement and revenue, it can make or break the success of your organization. You can use the Compass graphical client from MongoDB to visualize your database schema, collect information on optimization opportunities and make database changes to improve performance. In this talk, we will briefly introduce Compass and then delve into the features supporting database performance optimization. The talk will combine instruction on the use of Compass with recommendations for performance best practices. We will also review the detection and resolution of slow queries and excessive network utilization. After attending the talk, audience members will have a better understanding of the capabilities of Compass, including how those capabilities can be used to find and correct performance bottlenecks in MongoDB databases. This session is designed for those with limited MongoDB experience. Attendees should have a basic understanding of MongoDB’s schema design, the server/database/collection layout, and how their application accesses and uses the MongoDB database.
What You Will Learn:
- Identify excessive network utilization, adjust queries appropriately and use Compass to confirm results.
- Understand how the Compass graphical client can help you improve performance in your MongoDB deployment.
- Use Compass real time statistics to identify slow queries and recognize when a query is a good candidate for adding an index.
Using Compass to Diagnose Performance Problems in Your ClusterMongoDB
Using Compass to Diagnose Performance Problems in Your Cluster
Speaker: Brian Blevins, Technical Services Engineer, MongoDB
Date/Time: June 20, 1:50 PM
Track: Performance
Since the performance of your application drives engagement and revenue, it can make or break the success of your organization. You can use the Compass graphical client from MongoDB to visualize your database schema, collect information on optimization opportunities and make database changes to improve performance. In this talk, we will briefly introduce Compass and then delve into the features supporting database performance optimization. The talk will combine instruction on the use of Compass with recommendations for performance best practices. We will also review the detection and resolution of slow queries and excessive network utilization. After attending the talk, audience members will have a better understanding of the capabilities of Compass, including how those capabilities can be used to find and correct performance bottlenecks in MongoDB databases. This session is designed for those with limited MongoDB experience. Attendees should have a basic understanding of MongoDB’s schema design, the server/database/collection layout, and how their application accesses and uses the MongoDB database.
What You Will Learn:
- Identify excessive network utilization, adjust queries appropriately and use Compass to confirm results.
- Understand how the Compass graphical client can help you improve performance in your MongoDB deployment.
- Use Compass real time statistics to identify slow queries and recognize when a query is a good candidate for adding an index.
HIGH PERFORMANCE DATABASES
=> PERFORMANCE ANALYSIS
=> ALL ABOUT STORAGE & INDEXES
=> MANAGING MEMORY & LOCKS
=> QUERY OPTIMIZATION & TUNING
=> DATA MODELING
delivered to Stamford College Malaysia by Dr. Subramani Paramasivam
DBA – THINGS TO KNOW
=> BACKUP
=> RESTORE
=> DATA SECURITY
=> QUERY TUNING
=> MONITORING
=> INSTANCE MAINTENANCE
delivered to Stamford College Malaysia by Dr. Subramani Paramasivam
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Scalable Data Models with ElasticsearchBeyondTrees
At bol.com, a leading ecommerce platform in The Netherlands, we have done extensive research into what it would take to use ElasticSearch as the main search provider. We will explain the specific challenges and requirements of running an Elasticsearch cluster at bol.com-scale, and show how we have used generated data to do performance and scalability tests on different ways to model a hierarchical data model into Elasticsearch. We will describe the benefits and drawbacks of the different data model options, and their consequences for the design of the index and search applications.
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Successful artificial intelligence solutions always require a massive amount of high-quality labeled data. In most cases, we don’t have a large and qualitative labeled set together. Weak supervision and active learning tools may help you optimize the labeling process and address the shortage of data labels.
First, we will review how active learning can significantly reduce the amount of labeled data for training with classic approaches. We will show how active learning methods can be customized for a specific (NLP) task by using text embedding.
With weak supervision, we will see how using simple rules gets a big train dataset automatically and high model performance without manual labeling at all.
In the end, we will combine active learning and weak supervision by taking advantage of both techniques and achieving the best metrics.
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Tek Classes is one of the best etl testing training institute in bangalore.ETL Testing Training is designed for beginners to advanced Professionals. The course includes the following topics – Introduction to ETL Testing, OLTP vs. OLAP, Introduction to RDBMS, Database Testing Vs Data Warehouse Testing, Data Warehouse Workflow and Case Study, Data Checks using SQL and Scope of BI testing.
http://tekclasses.com/course/etl-testing-training/
How Clean is your Database? Data Scrubbing for all Skill SetsChad Petrovay
With staff working from home, many institutions are prioritizing data quality projects. Join Chad Petrovay, TMS Administrator at The Morgan Library & Museum, as he shares his deep knowledge of data scrubbing. Power users, system administrators, and SQL experts will learn how to correct and monitor data quality, and are introduced to new low-cost/free tools.
Why MongoDB over other Databases - HabilelabsHabilelabs
MongoDB is the faster-growing database. It is an open-source document and leading NoSQL database with the scalability and flexibility that you want with the querying and indexing that you need. In this Document, I presented why to choose MongoDB is over another database.
MYSQL Query Anti-Patterns That Can Be Moved to SphinxPythian
PalominoDB European Team lead, Vladimir Fedorkov will be discussing how to handle query bottlenecks that can result from increases in dataset and traffic
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A lot of database professionals have learned significantly about the issues arising in software projects that needed a Database Management System for storing information in the backend.
These are the slides from my presentation on Running R in the Database using Oracle R Enterprise. The second half of the presentation is a live demo of using the Oracle R Enterprise. Unfortunately the demo is not listed in these slides
There are many data modeling and database design terms and jargon that uses the word "key." Do you know the difference between a surrogate key and a primary key? A super key and a candidate key? Could you explain them to a technical audience? A business user or an auditor?
In this presentation, Karen Lopez covers the concepts of primary keys, foreign keys, candidate key, surrogate keys, and more.
Using Compass to Diagnose Performance Problems MongoDB
Speaker: Brian Blevins, Technical Services Engineer, MongoDB
Level: 200 (Intermediate)
Track: Performance
Since the performance of your application drives engagement and revenue, it can make or break the success of your organization. You can use the Compass graphical client from MongoDB to visualize your database schema, collect information on optimization opportunities and make database changes to improve performance. In this talk, we will briefly introduce Compass and then delve into the features supporting database performance optimization. The talk will combine instruction on the use of Compass with recommendations for performance best practices. We will also review the detection and resolution of slow queries and excessive network utilization. After attending the talk, audience members will have a better understanding of the capabilities of Compass, including how those capabilities can be used to find and correct performance bottlenecks in MongoDB databases. This session is designed for those with limited MongoDB experience. Attendees should have a basic understanding of MongoDB’s schema design, the server/database/collection layout, and how their application accesses and uses the MongoDB database.
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- Identify excessive network utilization, adjust queries appropriately and use Compass to confirm results.
- Understand how the Compass graphical client can help you improve performance in your MongoDB deployment.
- Use Compass real time statistics to identify slow queries and recognize when a query is a good candidate for adding an index.
Using Compass to Diagnose Performance Problems in Your ClusterMongoDB
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Speaker: Brian Blevins, Technical Services Engineer, MongoDB
Date/Time: June 20, 1:50 PM
Track: Performance
Since the performance of your application drives engagement and revenue, it can make or break the success of your organization. You can use the Compass graphical client from MongoDB to visualize your database schema, collect information on optimization opportunities and make database changes to improve performance. In this talk, we will briefly introduce Compass and then delve into the features supporting database performance optimization. The talk will combine instruction on the use of Compass with recommendations for performance best practices. We will also review the detection and resolution of slow queries and excessive network utilization. After attending the talk, audience members will have a better understanding of the capabilities of Compass, including how those capabilities can be used to find and correct performance bottlenecks in MongoDB databases. This session is designed for those with limited MongoDB experience. Attendees should have a basic understanding of MongoDB’s schema design, the server/database/collection layout, and how their application accesses and uses the MongoDB database.
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- Use Compass real time statistics to identify slow queries and recognize when a query is a good candidate for adding an index.
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=> ALL ABOUT STORAGE & INDEXES
=> MANAGING MEMORY & LOCKS
=> QUERY OPTIMIZATION & TUNING
=> DATA MODELING
delivered to Stamford College Malaysia by Dr. Subramani Paramasivam
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Show drafts
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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.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
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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
6. Point & Multipoint
• Spatial Data just representing the information about Location and
shape of the geometric objects.
• To locate a particular place, we can use the point with Latitude and
Longitude information.
• There are two spatial data types in SQL Server
• Geometry
• Geography
www.dageop.com
Optimizing Queries
7. Point & Multipoint
Syntax for Point
Point (Lat, Long, SRID)
Explanation:
Lat
• Is a float expression representing the x-coordinate of the Point being generated.
Long
• Is a float expression representing the y-coordinate of the Point being generated.
SRID
• Is an int expression representing the SRID of the geography instance you wish to
return.
www.dageop.com
Optimizing Queries
9. Point & Multipoint
• What is the purpose of using Point?
Helps to find the distance between the
two cites by using geography point with
lat & long information
www.dageop.com
Optimizing Queries
12. Range
• In SQL Server we have Range search condition, it will return all values
between two specified values.
• Normally two types of Ranges
• Inclusive Range
• Exclusive Range
• Inclusive Range
• It will use the keyword called “BETWEEN”
• Exclusive Range
• It will use greater than or lesser than
www.dageop.com
Optimizing Queries
14. Prefix Match & Ordering
www.dageop.com
Optimizing Queries
15. Prefix Match & Ordering
• Helps to identify first few character or values of an attribute.
• “Like” operator is used
• ORDER BY is used and will not affect the data.
• ASC
• DESC
www.dageop.com
Optimizing Queries
SELECT
FROM
WHERE COL1 LIKE PREFIXCOL1+’%’
ORDER BY LEN(PREFIXCOL1) DESC
17. Extremal
• 2 different extremes in a query, it can be Min or Max
• It’s used to find the maximum value of the particular column of the
table or minimum value.
• Syntax:
Min (Column_Name)
Max(Column_Name)
www.dageop.com
Optimizing Queries
20. Grouping
• Helps in grouping the data and helps finding value for a group.
• GROUP BY helps to aggregate other values in a query.
• CUBE and ROLLUP cannot be used without GROUP BY.
www.dageop.com
Optimizing Queries
23. Joins
• The SQL Joins is used to combine records from two or more tables.
• Why:
• To collect information with a common key column in tables
• Most commonly used joins are,
• Inner Join
• Outer Join
• Left Outer Join
• Right Outer Join
• Full Outer Join
www.dageop.com
Optimizing Queries
25. Joins
Left Outer Join:
Matching data from
both table as well as
everything from the table in
LEFT side.
Right Outer Join:
Matching data from
both table as well as
everything from the table in
RIGHT side.
www.dageop.com
Optimizing Queries
26. Joins
Full Outer Join
• Renders all the rows from both the tables based on the column used
to join.
www.dageop.com
Optimizing Queries