This document discusses basic SQL queries including selecting columns, sorting, filtering with WHERE clauses, combining conditions with AND and OR, aggregate functions like SUM and AVG, removing duplicates with DISTINCT, and grouping with GROUP BY. It includes examples of SQL queries and their corresponding analyses to demonstrate these fundamental SQL features.
SAS Ron Cody Solutions for even Number problems from Chapter 7 to 15Ayapparaj SKS
I have added answers to exercise sums (chapters 7 to 15 - even number problems) for Ron Cody's Learning SAS by Example Programmer's Guide.
In seventh chapter i gathered knowledge about using conditional statements such as IF, ELSE IF, WHERE, SELECT , sub-setting with the help of the above statements and using Boolean operators. In eighth chapter i gathered knowledge about using DO, DO WHILE, DO UNTIL along with LEAVE and CONTINUE statements and also to make a simple gplot. Ninth chapter talks about dealing with dates, finding the difference with respect to day, weekday, month, year and also computing difference quarterly, imputing missing values etc., using various functions and also to make qplot.Tenth chapter talks mainly about merging two datasets and subsetting using IN= function, updating a master table using another table and much more.Eleventh chapter talks about Functions to round and truncate numerical values, missing values, computing constant values, generating random values, to fetch values from previous observations etc.
Chapter twelve talks about functions dealing with manipulating characters. Chapter thirteen talks about array functions. Chapter Fourteen mainly deals with presenting the data. Fifteen is about generating reports.
SAS Ron Cody Solutions for even Number problems from Chapter 7 to 15Ayapparaj SKS
I have added answers to exercise sums (chapters 7 to 15 - even number problems) for Ron Cody's Learning SAS by Example Programmer's Guide.
In seventh chapter i gathered knowledge about using conditional statements such as IF, ELSE IF, WHERE, SELECT , sub-setting with the help of the above statements and using Boolean operators. In eighth chapter i gathered knowledge about using DO, DO WHILE, DO UNTIL along with LEAVE and CONTINUE statements and also to make a simple gplot. Ninth chapter talks about dealing with dates, finding the difference with respect to day, weekday, month, year and also computing difference quarterly, imputing missing values etc., using various functions and also to make qplot.Tenth chapter talks mainly about merging two datasets and subsetting using IN= function, updating a master table using another table and much more.Eleventh chapter talks about Functions to round and truncate numerical values, missing values, computing constant values, generating random values, to fetch values from previous observations etc.
Chapter twelve talks about functions dealing with manipulating characters. Chapter thirteen talks about array functions. Chapter Fourteen mainly deals with presenting the data. Fifteen is about generating reports.
After completing this lesson, you should be able to do the following:
Describe a view
Create a view
Retrieve data through a view
Alter the definition of a view
Insert, update, and delete data through a view
Drop a view
OLAP Basics and Fundamentals by Bharat Kalia Bharat Kalia
OLAP is a category of software technology that enables analysts, managers, and executives to gain insight into the data through fast, consistent, interactive, access in a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user.
Using restful APIs can be hard on your React applications. Before you know it, you are doing lots of parallel queries to the server. Using GraphQL instead of REST might help a lot. Instead of downloading many complete resources each component declares its own needs. Then the GraphQL client library then combines these requirements. The result is a single optimized query for the server. In this session, Maurice de Beijer is going to show you how to get started with GraphQL in your React applications.
After completing this lesson, you should be able
to do the following:
Describe a view
Create, alter the definition of, and drop a view
Retrieve data through a view
Insert, update, and delete data througha view
Create and use an inline view
Perform “Top-N” analysis
http://phpexecutor.com
YABench: A Comprehensive Framework for RDF Stream Processor Correctness and P...Maxim Kolchin
RDF stream processing (RSP) has become a vibrant area of research in the semantic web community. Recent advances have resulted in the development of several RSP engines that leverage semantics to facilitate reasoning over flows of incoming data. These engines vary greatly in terms of implemented query syntax, their evaluation and operational semantics, and in various performance dimensions. Existing benchmarks tackle particular aspects such as functional coverage, result correctness, or performance. None of them, however, assess RSP engine behavior comprehensively with respect to all these dimensions. In this paper, we introduce YABench, a novel benchmarking framework for RSP engines. YABench extends the concept of correctness checking and provides a flexible and comprehensive tool set to analyze and evaluate RSP engine behavior. It is highly configurable and provides quantifiable and reproducible results on correctness and performance characteristics. To validate our approach, we replicate results of the existing CSRBench benchmark with YABench. We then assess two well-established RSP engines, CQELS and C-SPARQL, through more comprehensive experiments. In particular, we measure precision, recall, performance, and scalability characteristics while varying throughput and query complexity. Finally, we discuss implications on the development of future stream processing engines and benchmarks.
Data Warehousing and Business Intelligence is one of the hottest skills today, and is the cornerstone for reporting, data science, and analytics. This course teaches the fundamentals with examples plus a project to fully illustrate the concepts.
Build systems orchestrate how human-readable source code is translated into executable programs. In a software project, source code changes can induce changes in the build system (aka. build co-changes). It is difficult for developers to identify when build co-changes are necessary due to the complexity of build systems. Prediction of build co-changes works well if there is a sufficient amount of training data to build a model. However, in practice, for new projects, there exists a limited number of changes. Using training data from other projects to predict the build co-changes in a new project can help improve the performance of the build co-change prediction. We refer to this problem as cross-project build co-change prediction.
In this paper, we propose CroBuild, a novel cross-project build co-change prediction approach that iteratively learns new classifiers. CroBuild constructs an ensemble of classifiers by iteratively building classifiers and assigning them weights according to its prediction error rate. Given that only a small proportion of code changes are build co-changing, we also propose an imbalance-aware approach that learns a threshold boundary between those code changes that are build co-changing and those that are not in order to construct classifiers in each iteration. To examine the benefits of CroBuild, we perform experiments on 4 large datasets including Mozilla, Eclipse-core, Lucene, and Jazz, comprising a total of 50,884 changes. On average, across the 4 datasets, CroBuild achieves a F1-score of up to 0.408. We also compare CroBuild with other approaches such as a basic model, AdaBoost proposed by Freund et al., and TrAdaBoost proposed by Dai et al.. On average, across the 4 datasets, the CroBuild approach yields an improvement in F1-scores of 41.54%, 36.63%, and 36.97% over the basic model, AdaBoost, and TrAdaBoost, respectively.
The ability to understand a user’s underlying needs is critical for conversational systems, especially with limited input from users in a conversation. Thus, in such a domain, Asking Clarification Questions (ACQs) to reveal users’ true intent from their queries or utterances arise as an essential task. However, it is noticeable that a key limitation of the existing ACQs studies is their incomparability, from inconsistent use of data, distinct experimental setups and evaluation strategies. Therefore, in this paper, to assist the development of ACQs techniques, we comprehensively analyse the current ACQs research status, which offers a detailed comparison of publicly available datasets, and discusses the applied evaluation metrics, joined with benchmarks for multiple ACQs-related tasks. In particular, given a thorough analysis of the ACQs task, we discuss a number of corresponding research directions for the investigation of ACQs as well as the development of conversational systems.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
After completing this lesson, you should be able to do the following:
Describe a view
Create a view
Retrieve data through a view
Alter the definition of a view
Insert, update, and delete data through a view
Drop a view
OLAP Basics and Fundamentals by Bharat Kalia Bharat Kalia
OLAP is a category of software technology that enables analysts, managers, and executives to gain insight into the data through fast, consistent, interactive, access in a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user.
Using restful APIs can be hard on your React applications. Before you know it, you are doing lots of parallel queries to the server. Using GraphQL instead of REST might help a lot. Instead of downloading many complete resources each component declares its own needs. Then the GraphQL client library then combines these requirements. The result is a single optimized query for the server. In this session, Maurice de Beijer is going to show you how to get started with GraphQL in your React applications.
After completing this lesson, you should be able
to do the following:
Describe a view
Create, alter the definition of, and drop a view
Retrieve data through a view
Insert, update, and delete data througha view
Create and use an inline view
Perform “Top-N” analysis
http://phpexecutor.com
YABench: A Comprehensive Framework for RDF Stream Processor Correctness and P...Maxim Kolchin
RDF stream processing (RSP) has become a vibrant area of research in the semantic web community. Recent advances have resulted in the development of several RSP engines that leverage semantics to facilitate reasoning over flows of incoming data. These engines vary greatly in terms of implemented query syntax, their evaluation and operational semantics, and in various performance dimensions. Existing benchmarks tackle particular aspects such as functional coverage, result correctness, or performance. None of them, however, assess RSP engine behavior comprehensively with respect to all these dimensions. In this paper, we introduce YABench, a novel benchmarking framework for RSP engines. YABench extends the concept of correctness checking and provides a flexible and comprehensive tool set to analyze and evaluate RSP engine behavior. It is highly configurable and provides quantifiable and reproducible results on correctness and performance characteristics. To validate our approach, we replicate results of the existing CSRBench benchmark with YABench. We then assess two well-established RSP engines, CQELS and C-SPARQL, through more comprehensive experiments. In particular, we measure precision, recall, performance, and scalability characteristics while varying throughput and query complexity. Finally, we discuss implications on the development of future stream processing engines and benchmarks.
Data Warehousing and Business Intelligence is one of the hottest skills today, and is the cornerstone for reporting, data science, and analytics. This course teaches the fundamentals with examples plus a project to fully illustrate the concepts.
Build systems orchestrate how human-readable source code is translated into executable programs. In a software project, source code changes can induce changes in the build system (aka. build co-changes). It is difficult for developers to identify when build co-changes are necessary due to the complexity of build systems. Prediction of build co-changes works well if there is a sufficient amount of training data to build a model. However, in practice, for new projects, there exists a limited number of changes. Using training data from other projects to predict the build co-changes in a new project can help improve the performance of the build co-change prediction. We refer to this problem as cross-project build co-change prediction.
In this paper, we propose CroBuild, a novel cross-project build co-change prediction approach that iteratively learns new classifiers. CroBuild constructs an ensemble of classifiers by iteratively building classifiers and assigning them weights according to its prediction error rate. Given that only a small proportion of code changes are build co-changing, we also propose an imbalance-aware approach that learns a threshold boundary between those code changes that are build co-changing and those that are not in order to construct classifiers in each iteration. To examine the benefits of CroBuild, we perform experiments on 4 large datasets including Mozilla, Eclipse-core, Lucene, and Jazz, comprising a total of 50,884 changes. On average, across the 4 datasets, CroBuild achieves a F1-score of up to 0.408. We also compare CroBuild with other approaches such as a basic model, AdaBoost proposed by Freund et al., and TrAdaBoost proposed by Dai et al.. On average, across the 4 datasets, the CroBuild approach yields an improvement in F1-scores of 41.54%, 36.63%, and 36.97% over the basic model, AdaBoost, and TrAdaBoost, respectively.
The ability to understand a user’s underlying needs is critical for conversational systems, especially with limited input from users in a conversation. Thus, in such a domain, Asking Clarification Questions (ACQs) to reveal users’ true intent from their queries or utterances arise as an essential task. However, it is noticeable that a key limitation of the existing ACQs studies is their incomparability, from inconsistent use of data, distinct experimental setups and evaluation strategies. Therefore, in this paper, to assist the development of ACQs techniques, we comprehensively analyse the current ACQs research status, which offers a detailed comparison of publicly available datasets, and discusses the applied evaluation metrics, joined with benchmarks for multiple ACQs-related tasks. In particular, given a thorough analysis of the ACQs task, we discuss a number of corresponding research directions for the investigation of ACQs as well as the development of conversational systems.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
50. Analysis: Using Where with Sum
Exhibit 9-37: Analysis of the Query that Uses
Sum with Where
51. Analysis: Using Where with Sum
Exhibit 9-37: Analysis of the Query that Uses
Sum with Where
52. Analysis: Using Where with Sum
Exhibit 9-37: Analysis of the Query that Uses
Sum with Where
53. Repeated Values
SELECT skill
FROM sky_member;
If you want to know the possible skills in the Member table,
this query lists them multiple times.
Exhibit 9-38: Selecting Skills
54. Using DISTINCT
SELECT DISTINCT skill
FROM sky_member
Distinct eliminates duplicate rows from the display.
Exhibit 9-39: Selecting Skills Using Distinct
55. Using GROUP BY
SELECT
FROM
GROUP BY
skill, Avg(jumps) AS ‘AverageJumps’
sky_member
skill;
Exhibit 9-41: The Design for the Query
Using Group By
59. Incorrect Use of GROUP BY
SELECT
FROM
skill, Avg(jumps) AS ‘AverageJumps’
sky_member;
GROUP BY must be used, and the same column must be used in the
GROUP BY and the aggregate function.
Exhibit 9-40: Error Generated by missing
GROUP BY specification
60. GROUP BY and HAVING
SELECT skill,
Avg(jumps) AS ‘AverageJumps’
FROM sky_member
GROUP BY skill
HAVING Avg(jumps) < 20
Having eliminates aggregations
that do not meet the conditions.
Exhibit 9-43: The Design for the Query
Using Having
64. HAVING and WHERE
SELECT skill,
Avg(jumps) AS ‘AverageJumps’
FROM sky_member
WHERE equip = ‘Y’
GROUP BY skill
HAVING Avg(jumps) > 10
Exhibit 9-45: Results for the Query Using
Having and Where
65. Analysis: HAVING and WHERE
Exhibit 9-46: The Analysis for the Query Using
Having and Where
66. Analysis: HAVING and WHERE
Exhibit 9-46: The Analysis for the Query Using
Having and Where
67. Analysis: HAVING and WHERE
Exhibit 9-46: The Analysis for the Query Using
Having and Where