This presentation discusses code generation capabilities in Oracle SQL Developer Data Modeler. Key features that support code generation include logical and relational modeling, domains, naming standards, and transformation scripts. The presenter demonstrates how to generate various types of code like entity rules, triggers, and packages by writing custom transformation scripts to query the model object and output code to files. Well-designed models can be transformed into maintainable application code automatically.
Top Five Cool Features in Oracle SQL Developer Data ModelerKent Graziano
This is the presentation I gave at OUGF14 in Helsinki, Finland in June 2014.
Oracle SQL Developer Data Modeler (SDDM) has been around for a few years now and is up to version 4.x. It really is an industrial strength data modeling tool that can be used for any data modeling task you need to tackle. Over the years I have found quite a few features and utilities in the tool that I rely on to make me more efficient (and agile) in developing my models. This presentation will demonstrate at least five of these features, tips, and tricks for you. I will walk through things like modifying the delivered reporting templates, how to create and applying object naming templates, how to use a table template and transformation script to add audit columns to every table, and using the new meta data export tool and several other cool things you might not know are there. Since there will likely be patches and new releases before the conference, there is a good chance there will be some new things for me to show you as well. This might be a bit of a whirlwind demo, so get SDDM installed on your device and bring it to the session so you can follow along.
This is the presentation for the talk I gave at JavaDay Kiev 2015. This is about an evolution of data processing systems from simple ones with single DWH to the complex approaches like Data Lake, Lambda Architecture and Pipeline architecture
Keeping Spark on Track: Productionizing Spark for ETLDatabricks
ETL is the first phase when building a big data processing platform. Data is available from various sources and formats, and transforming the data into a compact binary format (Parquet, ORC, etc.) allows Apache Spark to process it in the most efficient manner. This talk will discuss common issues and best practices for speeding up your ETL workflows, handling dirty data, and debugging tips for identifying errors.
Speakers: Kyle Pistor & Miklos Christine
This talk was originally presented at Spark Summit East 2017.
Modularized ETL Writing with Apache SparkDatabricks
Apache Spark has been an integral part of Stitch Fix’s compute infrastructure. Over the past five years, it has become our de facto standard for most ETL and heavy data processing needs and expanded our capabilities in the Data Warehouse.
Since all our writes to the Data Warehouse are through Apache Spark, we took advantage of that to add more modules that supplement ETL writing. Config driven and purposeful, these modules perform tasks onto a Spark Dataframe meant for a destination Hive table.
These are organized as a sequence of transformations on the Apache Spark dataframe prior to being written to the table.These include a process of journalizing. It is a process which helps maintain a non-duplicated historical record of mutable data associated with different parts of our business.
Data quality, another such module, is enabled on the fly using Apache Spark. Using Apache Spark we calculate metrics and have an adjacent service to help run quality tests for a table on the incoming data.
And finally, we cleanse data based on provided configurations, validate and write data into the warehouse. We have an internal versioning strategy in the Data Warehouse that allows us to know the difference between new and old data for a table.
Having these modules at the time of writing data allows cleaning, validation and testing of data prior to entering the Data Warehouse thus relieving us, programmatically, of most of the data problems. This talk focuses on ETL writing in Stitch Fix and describes these modules that help our Data Scientists on a daily basis.
Gradle is an open source build automation system that builds upon the concepts of Apache Ant and Apache Maven and introduces a Groovy-based domain-specific language (DSL) instead of the XML form used by Apache Maven for declaring the project configuration.
This document discusses Delta Change Data Feed (CDF), which allows capturing changes made to Delta tables. It describes how CDF works by storing change events like inserts, updates and deletes. It also outlines how CDF can be used to improve ETL pipelines, unify batch and streaming workflows, and meet regulatory needs. The document provides examples of enabling CDF, querying change data and storing the change events. It concludes by offering a demo of CDF in Jupyter notebooks.
This document provides an introduction to MongoDB, including what it is, why it may be used, and how its data model works. Some key points:
- MongoDB is a non-relational database that stores data in flexible, JSON-like documents rather than fixed schema tables.
- It offers advantages like dynamic schemas, embedding of related data, and fast performance at large scales.
- Data is organized into collections of documents, which can contain sub-documents to represent one-to-many relationships without joins.
- Queries use JSON-like syntax to search for patterns in documents, and indexes can improve performance.
Top Five Cool Features in Oracle SQL Developer Data ModelerKent Graziano
This is the presentation I gave at OUGF14 in Helsinki, Finland in June 2014.
Oracle SQL Developer Data Modeler (SDDM) has been around for a few years now and is up to version 4.x. It really is an industrial strength data modeling tool that can be used for any data modeling task you need to tackle. Over the years I have found quite a few features and utilities in the tool that I rely on to make me more efficient (and agile) in developing my models. This presentation will demonstrate at least five of these features, tips, and tricks for you. I will walk through things like modifying the delivered reporting templates, how to create and applying object naming templates, how to use a table template and transformation script to add audit columns to every table, and using the new meta data export tool and several other cool things you might not know are there. Since there will likely be patches and new releases before the conference, there is a good chance there will be some new things for me to show you as well. This might be a bit of a whirlwind demo, so get SDDM installed on your device and bring it to the session so you can follow along.
This is the presentation for the talk I gave at JavaDay Kiev 2015. This is about an evolution of data processing systems from simple ones with single DWH to the complex approaches like Data Lake, Lambda Architecture and Pipeline architecture
Keeping Spark on Track: Productionizing Spark for ETLDatabricks
ETL is the first phase when building a big data processing platform. Data is available from various sources and formats, and transforming the data into a compact binary format (Parquet, ORC, etc.) allows Apache Spark to process it in the most efficient manner. This talk will discuss common issues and best practices for speeding up your ETL workflows, handling dirty data, and debugging tips for identifying errors.
Speakers: Kyle Pistor & Miklos Christine
This talk was originally presented at Spark Summit East 2017.
Modularized ETL Writing with Apache SparkDatabricks
Apache Spark has been an integral part of Stitch Fix’s compute infrastructure. Over the past five years, it has become our de facto standard for most ETL and heavy data processing needs and expanded our capabilities in the Data Warehouse.
Since all our writes to the Data Warehouse are through Apache Spark, we took advantage of that to add more modules that supplement ETL writing. Config driven and purposeful, these modules perform tasks onto a Spark Dataframe meant for a destination Hive table.
These are organized as a sequence of transformations on the Apache Spark dataframe prior to being written to the table.These include a process of journalizing. It is a process which helps maintain a non-duplicated historical record of mutable data associated with different parts of our business.
Data quality, another such module, is enabled on the fly using Apache Spark. Using Apache Spark we calculate metrics and have an adjacent service to help run quality tests for a table on the incoming data.
And finally, we cleanse data based on provided configurations, validate and write data into the warehouse. We have an internal versioning strategy in the Data Warehouse that allows us to know the difference between new and old data for a table.
Having these modules at the time of writing data allows cleaning, validation and testing of data prior to entering the Data Warehouse thus relieving us, programmatically, of most of the data problems. This talk focuses on ETL writing in Stitch Fix and describes these modules that help our Data Scientists on a daily basis.
Gradle is an open source build automation system that builds upon the concepts of Apache Ant and Apache Maven and introduces a Groovy-based domain-specific language (DSL) instead of the XML form used by Apache Maven for declaring the project configuration.
This document discusses Delta Change Data Feed (CDF), which allows capturing changes made to Delta tables. It describes how CDF works by storing change events like inserts, updates and deletes. It also outlines how CDF can be used to improve ETL pipelines, unify batch and streaming workflows, and meet regulatory needs. The document provides examples of enabling CDF, querying change data and storing the change events. It concludes by offering a demo of CDF in Jupyter notebooks.
This document provides an introduction to MongoDB, including what it is, why it may be used, and how its data model works. Some key points:
- MongoDB is a non-relational database that stores data in flexible, JSON-like documents rather than fixed schema tables.
- It offers advantages like dynamic schemas, embedding of related data, and fast performance at large scales.
- Data is organized into collections of documents, which can contain sub-documents to represent one-to-many relationships without joins.
- Queries use JSON-like syntax to search for patterns in documents, and indexes can improve performance.
Oracle Database 19c, builds upon key architectural, distributed data and performance innovations established in earlier versions Oracle Database 12c and 18c releases. Oracle 19c has many new features, in this presentation we have covered below areas
Automated Installation, Configuration and Patching
AutoUpgrade and Database Utilities
Quelques Concepts de base à comprendre :
- Data Science
- Machines er Deep Learning, Les réseaux de neurones artificiels,
Les problèmes et les contraintes posées par les algorithmes d’apprentissage basés sur les réseaux de neurones
Principaux catalyseurs qui ont redynamisé l’intelligence artificielle:
- Calcul de hautes performances à savoir les architectures massivement parallèles et les systèmes distribués
- La Virtualisation et le cloud Computing
- Big Data, IOT et Applications Mobiles
- Framework et Algorithmes de Machines et Deep Learning
- Réseaux et Télécommunications
- Open source
L’écosystème des Framework de Machines et Deep Learning.
- L’architecture du Framwork TensorFlow
- Comment développer des applications de machines et Deep Learning pour les applications Web et Mobile en utilisant TensorFlow.JS.
- Démonstrations avec des liens pour télécharger le code source, allant de l’implémentation d’un simple perceptron en Java vers des modèles d’apprentissage supervisé multicouches de classification et un modèle d’extraction de caractéristiques à partir des images pour la reconnaissance des objets filmés par une caméra en utilisant des modèles CNN, pré-entraînes et exposés sur le cloud. (MobileNet)
MVC stands for Model-View-Controller. The MVC pattern separates an application into three parts: the model, the view, and the controller. The model handles the application's data logic, the view handles presentation logic, and the controller handles business logic and communication between the model and view. MVC is commonly used in PHP frameworks like CodeIgniter to separate an application's logical components.
This session covers how to work with PySpark interface to develop Spark applications. From loading, ingesting, and applying transformation on the data. The session covers how to work with different data sources of data, apply transformation, python best practices in developing Spark Apps. The demo covers integrating Apache Spark apps, In memory processing capabilities, working with notebooks, and integrating analytics tools into Spark Applications.
Hibernate is an object-relational mapping tool that allows developers to interact with a relational database (such as MySQL) using object-oriented programming. It provides functionality for persisting Java objects to tables in a database and querying those objects using HQL or SQL. Hibernate utilizes XML mapping files or annotations to define how Java classes map to database tables. A typical Hibernate application includes entity classes, mapping files, configuration files, and a controller class to manage persistence operations.
Spring Meetup Paris - Back to the basics of Spring (Boot)Eric SIBER
Aujourd'hui, avec Spring Boot, la promesse est de pouvoir bootstrapper en 60 secondes chrono une application.
C'est génial et porteur de sens (et un vrai laboratoire de bonnes pratiques) mais le temps de bootstrapping de l'équipe de développement, des individus composant cette équipe, est bien loin de suivre la même célérité. Dans le pire des cas, vous rencontrerez même des équipes au sein desquelles les écarts de niveaux sont extrêmement importants.
Pourquoi donc ? Réfléchissez à ce qui se passe si vous donnez le volant d'une formule 1 à quelqu'un qui vient tout juste d'obtenir son permis A après avoir fait son apprentissage sur une petite citadine dans une grande ville ... et vous aurez un début de réponse.
Le portfolio Spring constitue une excellente et populaire boîte à outils qui vous promet une grande productivité. Pour tirer profit de cette productivité et ne pas rester bridé par les connaissances de l'équipe, il ne suffit pas de choisir le bon framework, il faut savoir comment l'utiliser, le sublimer, en épousant les paradigmes de ce dernier.
Je vous propose donc d'en revenir aux fondamentaux de Spring (Boot) afin de vous permettre, bien avant de pouvoir vous attaquer au graal des architectures Microservices, d'être capable de tirer la pleine puissance du framework, tout du moins de ne pas en détourner l'essence.
Si vous n'êtes pas familier de Spring, ce talk vous permettra d'en avoir un premier aperçu pragmatique sans effet waouh. Si vous connaissez déjà Spring, vous trouverez à travers ce talk l'opportunité de prendre du recul sur son usage et de confronter vos pratiques aux patterns et bénéficies qu'il vous offre.
This presentation is based on Lawrence To's Maximum Availability Architecture (MAA) Oracle Open World Presentation talking about the latest updates on high availability (HA) best practices across multiple architectures, features and products in Oracle Database 19c. It considers all workloads, OLTP, DWH and analytics, mixed workload as well as on-premises and cloud-based deployments.
What to Expect From Oracle database 19cMaria Colgan
The Oracle Database has recently switched to an annual release model. Oracle Database 19c is only the second release in this new model. So what can you expect from the latest version of the Oracle Database? This presentation explains how Oracle Database 19c is really 12.2.0.3 the terminal release of the 12.2 family and the new features you can find in this release.
This document provides an overview of Kafka Connect and how it can be used to stream data between Kafka and other data systems. It discusses key Kafka Connect concepts like connectors, converters, transforms, deployment modes, and troubleshooting. The document contains configuration examples for connectors and transforms.
Change Data Capture to Data Lakes Using Apache Pulsar and Apache Hudi - Pulsa...StreamNative
Apache Hudi is an open data lake platform, designed around the streaming data model. At its core, Hudi provides a transactions, upserts, deletes on data lake storage, while also enabling CDC capabilities. Hudi also provides a coherent set of table services, which can clean, compact, cluster and optimize storage layout for better query performance. Finally, Hudi's data services provide out-of-box support for streaming data from event systems into lake storage in near real-time.
In this talk, we will walk through an end-end use case for change data capture from a relational database, starting with capture changes using the Pulsar CDC connector and then demonstrate how you can use the Hudi deltastreamer tool to then apply these changes into a table on the data lake. We will discuss various tips to operationalizing and monitoring such pipelines. We will conclude with some guidance on future integrations between the two projects including a native Hudi/Pulsar connector and Hudi tiered storage.
Migrating Oracle database to PostgreSQLUmair Mansoob
This document discusses migrating an Oracle database to PostgreSQL. It covers initial discovery of the Oracle database features and data types used. A migration assessment would analyze data type mapping, additional PostgreSQL features, and testing requirements. Challenges include porting PL/SQL code, minimizing downtime during migration, and comprehensive testing of applications on the new PostgreSQL platform. Migrating large data sets and ensuring performance for critical applications are also challenges.
webpack is a powerful module bundler and it becomes an essential part of our JavaScript Ecosystem. This ppt comprises an overview on webpack, some of the core concepts of webpack and it's configurations with some working examples.
Wide Column Store NoSQL vs SQL Data ModelingScyllaDB
NoSQL schemas are designed with very different goals in mind than SQL schemas. Where SQL normalizes data, NoSQL denormalizes. Where SQL joins ad-hoc, NoSQL pre-joins. And where SQL tries to push performance to the runtime, NoSQL bakes performance into the schema. Join us for an exploration of the core concepts of NoSQL schema design, using Scylla as an example to demonstrate the tradeoffs and rationale.
This document summarizes a presentation about unit testing Spark applications. The presentation discusses why it is important to run Spark locally and as unit tests instead of just on a cluster for faster feedback and easier debugging. It provides examples of how to run Spark locally in an IDE and as ScalaTest unit tests, including how to create test RDDs and DataFrames and supply test data. It also discusses testing concepts for streaming applications, MLlib, GraphX, and integration testing with technologies like HBase and Kafka.
* If you see the screen is not good condition, downloading please. *
Introduction to MariaDB
- mariadb oracle mysql comparison
- mariadb install step by step
- mariadb basic query
Oracle SQL Developer Data Modeler - Version Control Your DesignsJeff Smith
The document discusses Oracle SQL Developer Data Modeler, a free data modeling tool that allows collaborative design. It can be used to create logical data models, relational schemas, and physical implementations. The tool integrates with Subversion for version control, allowing multiple users to check out designs, track pending changes, and commit updates to a shared repository. It also facilitates comparing models to previous versions or data dictionaries.
Oracle Sql Developer Data Modeler 3 3 new featuresPhilip Stoyanov
This document discusses new features in Oracle SQL Developer Data Modeler version 3.3/4.0, including enhanced search functionality, improved handling of logical and relational models including surrogate keys and subtyping, and support for identity columns in Oracle Database. Key new features include global and model-level searching, setting common properties on search results, custom reports on search results, improved mapping of relationships and attributes to relational models, and configuration options for implementing entity hierarchies and generating dependent constraints.
Oracle Database 19c, builds upon key architectural, distributed data and performance innovations established in earlier versions Oracle Database 12c and 18c releases. Oracle 19c has many new features, in this presentation we have covered below areas
Automated Installation, Configuration and Patching
AutoUpgrade and Database Utilities
Quelques Concepts de base à comprendre :
- Data Science
- Machines er Deep Learning, Les réseaux de neurones artificiels,
Les problèmes et les contraintes posées par les algorithmes d’apprentissage basés sur les réseaux de neurones
Principaux catalyseurs qui ont redynamisé l’intelligence artificielle:
- Calcul de hautes performances à savoir les architectures massivement parallèles et les systèmes distribués
- La Virtualisation et le cloud Computing
- Big Data, IOT et Applications Mobiles
- Framework et Algorithmes de Machines et Deep Learning
- Réseaux et Télécommunications
- Open source
L’écosystème des Framework de Machines et Deep Learning.
- L’architecture du Framwork TensorFlow
- Comment développer des applications de machines et Deep Learning pour les applications Web et Mobile en utilisant TensorFlow.JS.
- Démonstrations avec des liens pour télécharger le code source, allant de l’implémentation d’un simple perceptron en Java vers des modèles d’apprentissage supervisé multicouches de classification et un modèle d’extraction de caractéristiques à partir des images pour la reconnaissance des objets filmés par une caméra en utilisant des modèles CNN, pré-entraînes et exposés sur le cloud. (MobileNet)
MVC stands for Model-View-Controller. The MVC pattern separates an application into three parts: the model, the view, and the controller. The model handles the application's data logic, the view handles presentation logic, and the controller handles business logic and communication between the model and view. MVC is commonly used in PHP frameworks like CodeIgniter to separate an application's logical components.
This session covers how to work with PySpark interface to develop Spark applications. From loading, ingesting, and applying transformation on the data. The session covers how to work with different data sources of data, apply transformation, python best practices in developing Spark Apps. The demo covers integrating Apache Spark apps, In memory processing capabilities, working with notebooks, and integrating analytics tools into Spark Applications.
Hibernate is an object-relational mapping tool that allows developers to interact with a relational database (such as MySQL) using object-oriented programming. It provides functionality for persisting Java objects to tables in a database and querying those objects using HQL or SQL. Hibernate utilizes XML mapping files or annotations to define how Java classes map to database tables. A typical Hibernate application includes entity classes, mapping files, configuration files, and a controller class to manage persistence operations.
Spring Meetup Paris - Back to the basics of Spring (Boot)Eric SIBER
Aujourd'hui, avec Spring Boot, la promesse est de pouvoir bootstrapper en 60 secondes chrono une application.
C'est génial et porteur de sens (et un vrai laboratoire de bonnes pratiques) mais le temps de bootstrapping de l'équipe de développement, des individus composant cette équipe, est bien loin de suivre la même célérité. Dans le pire des cas, vous rencontrerez même des équipes au sein desquelles les écarts de niveaux sont extrêmement importants.
Pourquoi donc ? Réfléchissez à ce qui se passe si vous donnez le volant d'une formule 1 à quelqu'un qui vient tout juste d'obtenir son permis A après avoir fait son apprentissage sur une petite citadine dans une grande ville ... et vous aurez un début de réponse.
Le portfolio Spring constitue une excellente et populaire boîte à outils qui vous promet une grande productivité. Pour tirer profit de cette productivité et ne pas rester bridé par les connaissances de l'équipe, il ne suffit pas de choisir le bon framework, il faut savoir comment l'utiliser, le sublimer, en épousant les paradigmes de ce dernier.
Je vous propose donc d'en revenir aux fondamentaux de Spring (Boot) afin de vous permettre, bien avant de pouvoir vous attaquer au graal des architectures Microservices, d'être capable de tirer la pleine puissance du framework, tout du moins de ne pas en détourner l'essence.
Si vous n'êtes pas familier de Spring, ce talk vous permettra d'en avoir un premier aperçu pragmatique sans effet waouh. Si vous connaissez déjà Spring, vous trouverez à travers ce talk l'opportunité de prendre du recul sur son usage et de confronter vos pratiques aux patterns et bénéficies qu'il vous offre.
This presentation is based on Lawrence To's Maximum Availability Architecture (MAA) Oracle Open World Presentation talking about the latest updates on high availability (HA) best practices across multiple architectures, features and products in Oracle Database 19c. It considers all workloads, OLTP, DWH and analytics, mixed workload as well as on-premises and cloud-based deployments.
What to Expect From Oracle database 19cMaria Colgan
The Oracle Database has recently switched to an annual release model. Oracle Database 19c is only the second release in this new model. So what can you expect from the latest version of the Oracle Database? This presentation explains how Oracle Database 19c is really 12.2.0.3 the terminal release of the 12.2 family and the new features you can find in this release.
This document provides an overview of Kafka Connect and how it can be used to stream data between Kafka and other data systems. It discusses key Kafka Connect concepts like connectors, converters, transforms, deployment modes, and troubleshooting. The document contains configuration examples for connectors and transforms.
Change Data Capture to Data Lakes Using Apache Pulsar and Apache Hudi - Pulsa...StreamNative
Apache Hudi is an open data lake platform, designed around the streaming data model. At its core, Hudi provides a transactions, upserts, deletes on data lake storage, while also enabling CDC capabilities. Hudi also provides a coherent set of table services, which can clean, compact, cluster and optimize storage layout for better query performance. Finally, Hudi's data services provide out-of-box support for streaming data from event systems into lake storage in near real-time.
In this talk, we will walk through an end-end use case for change data capture from a relational database, starting with capture changes using the Pulsar CDC connector and then demonstrate how you can use the Hudi deltastreamer tool to then apply these changes into a table on the data lake. We will discuss various tips to operationalizing and monitoring such pipelines. We will conclude with some guidance on future integrations between the two projects including a native Hudi/Pulsar connector and Hudi tiered storage.
Migrating Oracle database to PostgreSQLUmair Mansoob
This document discusses migrating an Oracle database to PostgreSQL. It covers initial discovery of the Oracle database features and data types used. A migration assessment would analyze data type mapping, additional PostgreSQL features, and testing requirements. Challenges include porting PL/SQL code, minimizing downtime during migration, and comprehensive testing of applications on the new PostgreSQL platform. Migrating large data sets and ensuring performance for critical applications are also challenges.
webpack is a powerful module bundler and it becomes an essential part of our JavaScript Ecosystem. This ppt comprises an overview on webpack, some of the core concepts of webpack and it's configurations with some working examples.
Wide Column Store NoSQL vs SQL Data ModelingScyllaDB
NoSQL schemas are designed with very different goals in mind than SQL schemas. Where SQL normalizes data, NoSQL denormalizes. Where SQL joins ad-hoc, NoSQL pre-joins. And where SQL tries to push performance to the runtime, NoSQL bakes performance into the schema. Join us for an exploration of the core concepts of NoSQL schema design, using Scylla as an example to demonstrate the tradeoffs and rationale.
This document summarizes a presentation about unit testing Spark applications. The presentation discusses why it is important to run Spark locally and as unit tests instead of just on a cluster for faster feedback and easier debugging. It provides examples of how to run Spark locally in an IDE and as ScalaTest unit tests, including how to create test RDDs and DataFrames and supply test data. It also discusses testing concepts for streaming applications, MLlib, GraphX, and integration testing with technologies like HBase and Kafka.
* If you see the screen is not good condition, downloading please. *
Introduction to MariaDB
- mariadb oracle mysql comparison
- mariadb install step by step
- mariadb basic query
Oracle SQL Developer Data Modeler - Version Control Your DesignsJeff Smith
The document discusses Oracle SQL Developer Data Modeler, a free data modeling tool that allows collaborative design. It can be used to create logical data models, relational schemas, and physical implementations. The tool integrates with Subversion for version control, allowing multiple users to check out designs, track pending changes, and commit updates to a shared repository. It also facilitates comparing models to previous versions or data dictionaries.
Oracle Sql Developer Data Modeler 3 3 new featuresPhilip Stoyanov
This document discusses new features in Oracle SQL Developer Data Modeler version 3.3/4.0, including enhanced search functionality, improved handling of logical and relational models including surrogate keys and subtyping, and support for identity columns in Oracle Database. Key new features include global and model-level searching, setting common properties on search results, custom reports on search results, improved mapping of relationships and attributes to relational models, and configuration options for implementing entity hierarchies and generating dependent constraints.
Christian Antognini presented on Oracle Database In-Memory at the 2015 DOAG conference in Nuremberg, Germany. He is a senior principal consultant, trainer, and partner at Trivadis, where he focuses on getting the most out of Oracle Database through logical and physical design, query optimization, and application performance management. The presentation provided an overview of Oracle Database In-Memory architecture, demonstrated its performance improvements through several scripts and videos, and noted that while speed ups can be significant, some scenarios may see reduced performance and there are limiting factors to consider.
Designing for Performance: Database Related Worst PracticesChristian Antognini
Christian Antognini presented on database-related worst practices for performance. He discussed 10 common worst practices including lack of logical and physical database design, implementing generic tables, not using constraints, wrong data types, unnecessary commits, and opening too many database connections. His core messages were that information technology is expensive so simple solutions are best for simple problems, performance is not an option and requires planning, and the right tools should be used for the job.
This document outlines Sieedah Francis's education and training from June 2012 to June 2013 which included certifications in digital literacy and Microsoft Office as well as courses in introduction to computers and programming, object-oriented programming, database modeling, SQL, PL/SQL, programming with Visual Basic, Java GUI development, and web design with HTML and Dreamweaver. Major projects included creating a database for an bookstore from an ERD diagram, programming a Visual Basic application to track company inventory and employees, and designing GUI applications in Java.
Every development shop is unique, and sometimes that uniqueness can hinder using tools. SQL Developer and Data Modeler have multiple mechanisms that allow for customizations. These customizations can range from simple to complex and can help tailor the tooling to any environment. Some are as simple as colored warning to remind the user what is production vs. development. Some could auto-generate code by walking over a data model. The most complex can change anything at all in the tool. Ever think of a command that should be in SQL Plus scripting? Want to auto-generate table APIs?
This document provides an overview and introduction to Oracle SQL Developer Data Modeler by Heli Helskyaho. Some key points:
- Data Modeler is a tool for database design that supports all phases of design from logical to physical modeling and generates DDL code.
- It allows designing, documenting, importing, exporting, reporting, versioning databases, and supports standards/rules.
- The presentation provides examples and explanations of logical models, relationships, transforming models between logical and relational, working with tables, columns, keys, and properties in physical design.
- Data Modeler can aid in agile database development and is concluded to be a good, free tool to use with support for documentation,
Your favorite data modeling tool, your partner in crime for Data Warehouse Au...FrederikN
The document discusses using a data modeling tool to automate the process of converting a logical data model to a physical data vault model. It provides examples of using the tool's built-in functionality to define properties, generate models, and make modifications without custom code. The goal is to leverage the tool's standard features to benefit from automation while supporting an incremental, multi-user approach to data vault modeling.
Delphix software installs as a VM and makes an initial copy of a source database using RMAN APIs. It then incrementally collects only changed data from the source database and compresses it, typically to 1/3 the size. This allows virtual databases to be provisioned from any point in the captured change data within a configurable retention window (typically 2 weeks). This allows development databases to be spun up in minutes with minimal storage, avoiding duplicating database contents across environments.
Yes, Oracle SQL Developer allows you to make a JDBC connection to SQL Server. Here's a quick overview of things you can do, plus a reminder that it's also the official migration platform for Oracle Database migrations.
PL/SQL All the Things in Oracle SQL DeveloperJeff Smith
The document discusses features of Oracle SQL Developer, a free Oracle Database IDE. It provides an overview of SQL Developer's major feature areas including its PL/SQL IDE capabilities, SQL editing, database object browsing, reporting, data modeling, administration, and more. The document also reviews SQL Developer's history and includes screenshots demonstrating features like snippets, formatting, debugging, documentation generation, and unit testing.
Worst Practices in Data Warehouse DesignKent Graziano
This presentation was given at OakTable World 2014 (#OTW14) in San Francisco. After many years of designing data warehouses and consulting on data warehouse architectures, I have seen a lot of bad design choices by supposedly experienced professional. A sense of professionalism, confidentiality agreements, and some sense of common decency have prevented me from calling people out on some of this. No more! In this session I will walk you through a typical bad design like many I have seen. I will show you what I see when I reverse engineer a supposedly complete design and walk through what is wrong with it and discuss options to correct it. This will be a test of your knowledge of data warehouse best practices by seeing if you can recognize these worst practices.
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.
The presentation Dr Peter Black delivered at the Improving IT/IM Infrastructure Decisions Seminar in Aberdeen, May 2013. This gives a brief overview of the standards within the Oil & Gas Industry for Production Allocation, including PRODML and REST and why they are so important
SQL Developer isn't just for...developers!
SQL Developer doubles the features available to the end user with the DBA panel, accessible from the View menu.
Pennsylvania Banner User Group Webinar: Oracle SQL Developer Tips & TricksJeff Smith
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3. Code Generation with Oracle SDDM
Presenter information
VX Company IT Services b.v.
Email rvdberg@vxcompany.com
Twitter twitter.com/rob_vd_berg
Blog rvdbergblog.wordpress.com
4. Code Generation with Oracle SQL Developer Data Modeler
Introduction
SDDM Features supporting Code Generation
Code Generation
Agenda
6. Oracle SQL Developer Data Modeler
Main Features - Introduction
Last version as of December 21, 2015 is 4.1.3.901
Free
Create, browse and edit relational models
Create, browse and edit multi-dimensional models
Forward and reverse engineering
Integrated source code control
Since July 1, 2009
Jeff Smith
Heli Helskyaho
7. – This presentation is about Application Code Generation
– Focussing on:
application code that implements Business Rules
– Code as complex as Entity Rules, Inter Entity Rules,
Change Event Rules, etc.
– In short: Code Beyond DDL
– Supporting: thick database design
(Toon Koppelaars, Bryn Llewellyn)
Oracle SQL Developer Data Modeler
Main Features – Introduction – Presentation subject
8. Oracle SQL Developer Data Modeler
Setting the stage
Features
supporting
Application
Code
Generation
9. Oracle SQL Developer Data Modeler
Features – “Oh, pretty pictures – I can do that !!”
10. – ER diagramming (Barker, Bachman or IE notation)
– Engineering to and from relational models
– Supertypes and subtypes, arcs
– Compare and merge with logical model in another design
Oracle SQL Developer Data Modeler
Features – Logical Models
14. – Importing – from Designer, Erwin (XML) and more
– Version Support – team development (Subversion)
Oracle SQL Developer Data Modeler
Features – import your model, put it in your VCS
17. Oracle SQL Developer Data Modeler
Features – Logical Models – Actual Footage ! Corporate Identity applied !
Barker (Oracle Designer)
18. Oracle SQL Developer Data Modeler
Features – Logical Models – Actual Footage ! Corporate Identity applied !
Bachman
Barker (Oracle Designer)
Information Engineering
19. – Subviews can be used to represent objects related to given
subject area
– Subviews can be nested(linked) thus allowing to build
network (or hierarchy) of related subviews – navigation
between linked subviews is supported
Oracle SQL Developer Data Modeler
Features - Subject Area Management
22. – Name structure for elements in Logical and Relational
models
– Model level restrictions for – name length, possible
characters, used letter case
– Name translation during engineering between logical and
relational models
– Naming templates for table constraints and indexes
– Prefix management
Oracle SQL Developer Data Modeler
Features – Naming Standards
23. Oracle SQL Developer Data Modeler
Features – Naming Standards
Right mouse menu on design
properties->settings->naming standard
25. – Supports validation rules;
– Easy exchange and synchronization of domains;
– Assignable to group of attributes and columns
in different models;
– Set “Default value” property at domain level. This can be
updated at column/attribute level.
Oracle SQL Developer Data Modeler
Features - Domains
Example:
domain = Country Codes
mandatory = True
values = (NL = The Netherlands, UK = Great Britain)
default = NL
26. Oracle SQL Developer Data Modeler
Features – Object Model
The design is stored as an object model
That can be manipulated programmatically
(which is NOT the subject of this presentation)
That can be queried programmatically
Which enables you to synthesize PL/SQL scripts based on the
design and each detailed property of the design
29. – A few transformation scripts are pre-supplied
– Like adding default columns to a range of tables, or
– Like re-ordering columns after engineering from logical
– Add your own transformation scripts
Oracle SQL Developer Data Modeler
Features – Relational Models – Transformation scripts
30. Oracle SQL Developer Data Modeler
Features – Relational Model – Custom Transformation Scripts
31. Oracle SQL Developer Data Modeler
Features – Relational Model – Custom Transformation Scripts
35. – Add a custom transformation
– Set it up to output modeled design to the file system
– Model design properties
– Dynamic properties
Oracle SQL Developer Data Modeler
How to generate code
39. // Code shown below is JavaScript //
runFile = new java.io.FileWriter(appfolder+appname+ "_CREATE_ALL.sql");
run = new java.io.PrintWriter(runFile);
run.println("-----------------------------------------------------------");
run.println("-- Version : 1.0.0");
run.println("-- Proces : Zorgsturing");
run.println("-- File-name : <app_alias>_CREATE_ALL.sql");
run.println("-- Creator : Rob van den Berg");
run.println("-- Creation date : "+ today);
run.println("-- Description : Master create script");
run.println("--");
Oracle SQL Developer Data Modeler
Basic setup: include a master script
41. // Code shown below is (PL/)SQL //
/*
Maintain table level constraints on ZST_VERTALINGEN_MPG
Change History
Who When What
---------------- ---------- --------
Rob van den Berg 12/05/2016 Creation
*/
-- define check constraints
alter table ZST_VERTALINGEN_MPG add constraint ZST_BR_VMP003_TPL check
(datum_einde is null or (datum_ingang <= datum_einde));
Oracle SQL Developer Data Modeler
Implementation of Tuple Rule
Generated script
ZST_VMP_CON.sql
44. – typical Entity Rule “rows should not OVERLAP”
– Which typically presumes data definition of
– Column start_date NOT NULL
– Column end_date
– Some unique key including start_date
next to (optional) columns identifying the group
– Analist would phrase the rule like
“Thou shalt not enter any conflicting time period (for the
group of tuples identified by X, Y, Z”
Oracle SQL Developer Data Modeler
Definition of Entity Rule
45. Oracle SQL Developer Data Modeler
Definition of Entity Rule: specify the name identifying the rule
46. // Code shown below is (PL/)SQL //
/*
Maintain Table ZST_VERTALINGEN_MPG
…
…
--define unique key(s)
alter table ZST_VERTALINGEN_MPG add constraint zst_vmp_un1 unique
( bte_id
, mpg_code
, oms_extern
, datum_ingang
);
Oracle SQL Developer Data Modeler
Implementation of Entity Rule: unique key including start_date
Generated script
ZST_VMP_TAB.sql
START_DATE
OVERLAP can still occur
47. // Code shown below is (PL/)SQL //
create or replace trigger ZST_VMP_BIR
before insert on ZST_VERTALINGEN_MPG
for each row
begin
-- support insert change event
ZST_VMP_PCK.trg_bir
( p_id => :new.ID
…
, p_datum_ingang => :new.DATUM_INGANG
…
, p_datum_einde => :new.DATUM_EINDE
);
end;
/
Oracle SQL Developer Data Modeler
Implementation of Entity Rule
Generated script
ZST_VMP_TRG.sql
START_DATE
END_DATE
48. – Triggers generated within each trigger definition file:
– Before insert, update, delete statement
– After insert, update, delete statement
– Same for row triggers
Oracle SQL Developer Data Modeler
Implementation of Entity Rule
49. // Code shown below is (PL/)SQL //
procedure trg_ais
is
begin
check_br_vmp002_ent;
…
end trg_ais;
Oracle SQL Developer Data Modeler
Implementation of Entity Rule
Generated script
ZST_VMP_PCK.sql
50. // Code shown below is (PL/)SQL //
…
procedure check_br_vmp002_ent
is
-- Check if any time overlap check does not get violated
-- find record overlapping in time
cursor c_overlap
( v_not_id in ZST_VERTALINGEN_MPG.ID%TYPE
, v_bte_id in ZST_VERTALINGEN_MPG.BTE_ID%TYPE
, v_mpg_code in ZST_VERTALINGEN_MPG.MPG_CODE%TYPE
…
…
Oracle SQL Developer Data Modeler
Implementation of Entity Rule
Generated script
ZST_VMP_PCK.sql
52. Generating code
– Is efficient
– Follows standards and guidelines
– Leads to maintainable code. No exceptions.
Oracle SQL Developer Data Modeler
Conclusions
57. Code Generation with Oracle SDDM
Presenter information
VX Company IT Services b.v.
Email rvdberg@vxcompany.com
Twitter twitter.com/rob_vd_berg
Blog rvdbergblog.wordpress.com
Editor's Notes
Welcome to my session, It’s the last session of the day, so Hello everybody welcome, welcome welcome, I will introduce myself and get going with the presentation. How are you guys doing today, everybody allright ?
Let’s see. My session will cover Oracle PL/SQL code generation with Oracle SQL Developer Data Modeler in the role of the generator of this code. My name is Rob van den Berg.
I have been working for VX Company for the last 18 years. VX Company is a Oracle Platinum Partner. I have worked on major Oracle implementations as a contractor, in particular for Oracle, whom I’ve been hired to for fourteen years. I have an email address, a twitter handle and a blog site.
These are the main sections my presentation can be subdivided into. I’ll start with an introduction, like I’m doing right now, I’ll cover the features I think are crucial to allow Oracle SQL Data Modeler to generate custom application code, and finally I will get to the core of my presentation showing real examples of generated code and how it was setup that way.
No let me tell you right off the bat, this is not a theoretical lecture of how things might be setup. I’ve used Oracle SQL Developer Data Modeler in a production situation to generate complex business rules. And I will show you how.
First I will finish my introduction of this code generation theme I would like to discuss.
The tool itself has evolved in the last years from a both licensed and not so comprehensive designer tool to a free tool with extensive options. It’s in existence sinds 2009, current version is 4.1.3. Both Jeff Smith and Heli Helskyaho have greatly contributed in explaining how this tool can be used. Heli wrote a book published by Oracle Press on the subject. THAT Jeff Smith owns a lively blog site on both SQL Developer and SQL Developer Data Modeler.
My presentation zooms in on just one feature of the tool, the code generation feature. I focus on application code that implements Business Rules, which can be as complex as Entity Rules, Inter Entity Rules, Change Event Rules etcetera. In short: that’s application code which cannot be generated out of the box straight from your design. It goes beyond DDL.
Now I have to pause here for a second. Why would we generate application code ? I can explain why I wanted to. It’s because I’m a do-everything-in-the-database guy, just like Toon Koppelaar and Bryn Llewellyn want us to. I’ve you have been able to attend to Bryn’s session, like he did today and many times before, you exactly know why. However, implementing business rules in the database can get repetitive by nature. If you code by hand, you might end up coding the same pattern over and over again. I know because it happened to me. And it happened to me AFTER I had been accustomed to generate application code from within Designer and Headstart for more than ten years. Suddenly I had been hired by a company that couldn’t offer me these tools. So I had to come up with my own replacement for Designer and Headstart. Maybe that situation sounds familiar to anyone?
So which features did I stumble upon which got me going ?
The CORE feature of SQL Developer Data Modeler of course is to visually model a database design. Click and drag entities or tables, draw lines between them representing relationships between them. Rearrange entities, create subviews.
Entity Relationship Modeling with notations which can at the design level be changed according to the visualization method you are used to, like Barker, the notation you know from good old Oracle Designer, or Bachman or Information Engineering. Having designed a logical model, you can generate a relational model from it, or reverse engineer the other way around. Common design features like Supertypes, subtypes and arcs are fully supported. You can open multiple designs in parallel and compare differences between them.
I would certainly encourage you to explore different kinds of modeling which you might not expect SQL Developer Data Modeler would also support.
SQL Developer Data Modeler is a database design and generation tool, supporting a model driven application development approach, not limited to just entity-relationship and data modeling, but also other types of models
This picture presents one example where you can find other types of models in the menu
But wait! We might already HAVE modeled a database design, be it a full entity relationship model, a data model or just an existing data dictionary. Well we can import these models. We can import a data dictionary. For that matter, we can also export these models too. Should you be working in a team, SQL Developer Data Modeler supports putting the desig in version control, using Subversion.
This is a bit of a side step. This is NOT a strictly indispensable feature enabling code generation that we WILL discuss very soon now, but it’s too cool not to mention now
Here we have it: your first logical model build from scratch. But the colours…they more or less precisely lack any corporate identity. Which might or might not be an issue. But if it is…
Open Design Properties – Settings – Diagram – Format. As you can see any distinguishable object can be set up to appear in any colour or font as you like. Doubleclick the colour and you see how. The other tab pages present other ways to specify a colour, including entering exact colour numbers.
The result is the apply corporate identity, in this case VX Corporate identiy.
Work with a notation method you are most used to.
In case your design holds really a lot of tables, like more than fifteen, you will be interested in using a method to group tables together visually, which can be done using subviews. Subviews can be given a name.
Like a “Core” subview, showing the “Core” tables and their relations of the application
When you work in a team, you often have to adhere to the company’s coding standards. Make sure coding standards are in place.
Oracle SQL Developer Data Modeler supports configuring and applying these standards. You can setup a naming structure for entities and for tables. You can restrict the length of names for your design. You can restrict which characters are used in a entity name to separate different parts of the name. You can set up how constraints and indexes are named as a derivative of the names or aliases of the tables.
So just open design properties – settings – naming standard and find many options providing you the option to specify standards.
Example of a standard about not using a forbidden word
If you have just imported a design, it might just not be perfect already. Like this one. This one has been resized an aligned on a grid, using the graphical interface, but the content has also been polished quite a bit. I needed tuning.
That’s where Optimus Prime kicks in. Autobot.
Your design might have table name not all upper case. You can apply a pre-supplied transformation script to modify all tables names to upper case.
Or rearrange the order of columns. If you want to start working with transformation scripts, reading and interpreting the pre-supplied scripts, which aren’t very complex, is a good start. As you, a transformation script has a name, and an engine it is supposed to run in. This can be ruby or Mozilla rhino for older versions of Java. If you already have Java 8, the engine is called Nashorn.
You can find more information on Oracle Nashorn on OTN.
Finally we get to the core.
If you need to find out which methods are available, here’s where you can find more information. After installation, you will find the information in the datamodeler/xmlmetadata folder. The html file index.html gives access to all the information which you can browse through.
Create the script, give it a name, and run it by clicking ‘ apply’.
Create a runFile FileWriter object, a run PrintWriter object and call the println method.
Just for the sake of this example, let us generate code to maintain a check constraint. Of course, there’s still no need to do it this way, check constraints are just a maintanable part of a design and code can already be generated for it out of the box, but suppose we need separate files for check constraints belonging to each table with commentary header like shown.
Pass the println method to a previously defined con PrintWriter object for all check constraints of all tables.
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