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An overview of reference architectures for PostgresEDB
EDB Reference Architectures are designed to help new and existing users alike to quickly design a deployment architecture that suits their needs. They can be used as either the blueprint for a deployment, or as the basis for a design that enhances and extends the functionality and features offered.
Add-on architectures allow users to easily extend their core database server deployment to add additional features and functionality "building block" style.
In this webinar, we will review the following architectures:
- Single Node
- Multi Node with Asynchronous Replication
- Multi Node with Synchronous Replication
- Add-on Architectures
Speaker:
Michael Willer
Sales Engineer, EDB
Make Your Application “Oracle RAC Ready” & Test For ItMarkus Michalewicz
This presentation talks about the secrets behind Oracle RAC’s horizontal scaling algorithm, Cache Fusion, and how you can ensure that your application is “Oracle RAC ready.”. It discusses do's and don'ts and how to test your application for "Oracle RAC readiness". This version was first presented in Sangam19.
RubiX: A caching framework for big data engines in the cloud. Helps provide data caching capabilities to engines like Presto, Spark, Hadoop, etc transparently without user intervention.
An overview of reference architectures for PostgresEDB
EDB Reference Architectures are designed to help new and existing users alike to quickly design a deployment architecture that suits their needs. They can be used as either the blueprint for a deployment, or as the basis for a design that enhances and extends the functionality and features offered.
Add-on architectures allow users to easily extend their core database server deployment to add additional features and functionality "building block" style.
In this webinar, we will review the following architectures:
- Single Node
- Multi Node with Asynchronous Replication
- Multi Node with Synchronous Replication
- Add-on Architectures
Speaker:
Michael Willer
Sales Engineer, EDB
Make Your Application “Oracle RAC Ready” & Test For ItMarkus Michalewicz
This presentation talks about the secrets behind Oracle RAC’s horizontal scaling algorithm, Cache Fusion, and how you can ensure that your application is “Oracle RAC ready.”. It discusses do's and don'ts and how to test your application for "Oracle RAC readiness". This version was first presented in Sangam19.
RubiX: A caching framework for big data engines in the cloud. Helps provide data caching capabilities to engines like Presto, Spark, Hadoop, etc transparently without user intervention.
Whether migrating a database or application from Oracle to Postgres, as a first step, we need to analyze the database objects(DDLs), to find out the incompatibilities between both the databases and estimate the time and cost required for the migration. In schema migration, having a good knowledge of Oracle and Postgres helps to identify incompatibilities and choose the right tool for analysis/conversion. In this webinar, we will discuss schema incompatibility hurdles when migrating from Oracle to Postgres and how to overcome them.
What you will learn in this webinar:
- How you identify if your oracle schema is compatible with PostgreSQL
- Incompatibility hurdles and identifying them with Migration tools
- How to Overcome incompatibility hurdles
- Available tools for conversion
- Post migration activities - functional testing, performance analysis, data migration, application switchover
A look at what HA is and what PostgreSQL has to offer for building an open source HA solution. Covers various aspects in terms of Recovery Point Objective and Recovery Time Objective. Includes backup and restore, PITR (point in time recovery) and streaming replication concepts.
Oracle RAC Virtualized - In VMs, in Containers, On-premises, and in the CloudMarkus Michalewicz
This presentation discusses the support guidelines for using Oracle Real Application Clusters (RAC) in virtualized environments, for which general Oracle Database support guidelines are discussed shortly first.
First presented during DOAG 2021 User Conference, this presentation replaces its predecessor from 2016: https://www.slideshare.net/MarkusMichalewicz/how-to-use-oracle-rac-in-a-cloud-a-support-question
This presentation provides a clear overview of how Oracle Database In-Memory optimizes both analytics and mixed workloads, delivering outstanding performance while supporting real-time analytics, business intelligence, and reporting. It provides details on what you can expect from Database In-Memory in both Oracle Database 12.1.0.2 and 12.2.
Vectorized UDF: Scalable Analysis with Python and PySpark with Li JinDatabricks
Over the past few years, Python has become the default language for data scientists. Packages such as pandas, numpy, statsmodel, and scikit-learn have gained great adoption and become the mainstream toolkits. At the same time, Apache Spark has become the de facto standard in processing big data. Spark ships with a Python interface, aka PySpark, however, because Spark’s runtime is implemented on top of JVM, using PySpark with native Python library sometimes results in poor performance and usability.
In this talk, we introduce a new type of PySpark UDF designed to solve this problem – Vectorized UDF. Vectorized UDF is built on top of Apache Arrow and bring you the best of both worlds – the ability to define easy to use, high performance UDFs and scale up your analysis with Spark.
"Extended" or "Stretched" Oracle RAC has been available as a concept for a while. Oracle RAC 12c Release 2 introduces an Oracle Extended Cluster configuration, in which the cluster understands the concept of sites and extended setups. This knowledge is used to more efficiently manage "Extended Oracle RAC", whether the nodes are 0.1 mile or 10 miles apart.
The presentation was last updated on August 7th 2017 to add a reference to the new MAA White Paper: "Installing Oracle Extended Clusters on Exadata Database Machine" - http://www.oracle.com/technetwork/database/availability/maa-extclusters-installguide-3748227.pdf and to correct some minor details.
Re-imagine Data Monitoring with whylogs and SparkDatabricks
In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data.
In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.
Deep Dive on Amazon Aurora with PostgreSQL Compatibility (DAT305-R1) - AWS re...Amazon Web Services
Amazon Aurora with PostgreSQL Compatibility is a relational database service that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. We review the functionality in order to understand the architectural differences that contribute to improved scalability, availability, and durability. We also dive deep into the capabilities of the service and review the latest available features. Finally, we walk through the techniques that can be used to migrate to Amazon Aurora.
Designing Apache Hudi for Incremental Processing With Vinoth Chandar and Etha...HostedbyConfluent
Designing Apache Hudi for Incremental Processing With Vinoth Chandar and Ethan Guo | Current 2022
Back in 2016, Apache Hudi brought transactions, change capture on top of data lakes, what is today referred to as the Lakehouse architecture. In this session, we first introduce Apache Hudi and the key technology gaps it fills in the modern data architecture. Bridging traditional data lakes and warehouses, Hudi helps realize the Lakehouse vision, by bringing transactions, optimized table metadata to data lakes and powerful storage layout optimizations, moving them closer to cloud warehouses of today. Viewed from a data engineering lens, Hudi also plays a key unifying role between the batch and stream processing worlds, by acting as a columnar, server-less ""state store"" for batch jobs, ushering in what we call the incremental processing model, where batch jobs can consume new data, update/delete intermediate results in a Hudi table, instead of re-computing/re-write entire output like old-school big batch jobs.
Rest of talk focusses on a deep dive into the some of the time-tested design choices and tradeoffs in Hudi, that helps power some of the largest transactional data lakes on the planet today. We will start by describing a tour of the storage format design, including data, metadata layouts and of course Hudi's timeline, an event log that is central to implementing ACID transactions and concurrency control. We will delve deeper into the practical concurrency control pitfalls in data lakes, and show how Hudi's hybrid approach combining MVCC with optimistic concurrency control, lowers contention and unlocks minute-level near real-time commits to Hudi tables. We will conclude with code examples that showcase Hudi's rich set of table services that perform vital table management such as cleaning older file versions, compaction of delta logs into base files, dynamic re-clustering for faster query performance, or the more recently introduced indexing service that maintains Hudi's multi-modal indexing capabilities.
"It can always get worse!" – Lessons Learned in over 20 years working with Or...Markus Michalewicz
First presented during the DOAG 2022 Conference and Exhibition, this presentation discusses and reviews the most significant lessons learned in over 20 years of working with Oracle Maximum Availability Architecture. It explains why documentation is good, but automated checks are better, and why standardization can help increase the availability of nearly all systems, including database systems.
Automating a PostgreSQL High Availability Architecture with AnsibleEDB
Highly available databases are essential to organizations depending on mission-critical, 24/7 access to data. Postgres is widely recognized as an excellent open-source database, with critical maturity and features that allow organizations to scale and achieve high availability.
EDB reference architectures are designed to help new and existing users alike to quickly design a deployment architecture that suits their needs. Users can use these reference architectures as a blueprint or as the basis for a design that enhances and extends the functionality and features offered.
This webinar will explore:
- Concepts of High Availability
- Quick review of EDB reference architectures
- EDB tools to create a highly available PostgreSQL architecture
- Options for automating the deployment of reference architectures
- EDB Ansible® roles helping in automating the deployment of reference architectures
- Features and capabilities of Ansible roles
- Automating the provisioning of the resources in the cloud using Terraform™
How to build a streaming Lakehouse with Flink, Kafka, and HudiFlink Forward
Flink Forward San Francisco 2022.
With a real-time processing engine like Flink and a transactional storage layer like Hudi, it has never been easier to build end-to-end low-latency data platforms connecting sources like Kafka to data lake storage. Come learn how to blend Lakehouse architectural patterns with real-time processing pipelines with Flink and Hudi. We will dive deep on how Flink can leverage the newest features of Hudi like multi-modal indexing that dramatically improves query and write performance, data skipping that reduces the query latency by 10x for large datasets, and many more innovations unique to Flink and Hudi.
by
Ethan Guo & Kyle Weller
Migrating a data intensive microservice from Python to GoNikolay Stoitsev
As Uber is hyper-growing as a company so does our need for scalable and resilient systems. In this talk, I’m going to tell the story of how my team migrated from Python to Go, a microservice that processes millions of events every day. First, we are going to start with the rationale behind the migration. Then we are going to go over the Python and Go tech stacks that we use. Last but not least, I’m also going to share our approach for migrating the service while running in production, adding new features and making sure there are no regressions.
Whether migrating a database or application from Oracle to Postgres, as a first step, we need to analyze the database objects(DDLs), to find out the incompatibilities between both the databases and estimate the time and cost required for the migration. In schema migration, having a good knowledge of Oracle and Postgres helps to identify incompatibilities and choose the right tool for analysis/conversion. In this webinar, we will discuss schema incompatibility hurdles when migrating from Oracle to Postgres and how to overcome them.
What you will learn in this webinar:
- How you identify if your oracle schema is compatible with PostgreSQL
- Incompatibility hurdles and identifying them with Migration tools
- How to Overcome incompatibility hurdles
- Available tools for conversion
- Post migration activities - functional testing, performance analysis, data migration, application switchover
A look at what HA is and what PostgreSQL has to offer for building an open source HA solution. Covers various aspects in terms of Recovery Point Objective and Recovery Time Objective. Includes backup and restore, PITR (point in time recovery) and streaming replication concepts.
Oracle RAC Virtualized - In VMs, in Containers, On-premises, and in the CloudMarkus Michalewicz
This presentation discusses the support guidelines for using Oracle Real Application Clusters (RAC) in virtualized environments, for which general Oracle Database support guidelines are discussed shortly first.
First presented during DOAG 2021 User Conference, this presentation replaces its predecessor from 2016: https://www.slideshare.net/MarkusMichalewicz/how-to-use-oracle-rac-in-a-cloud-a-support-question
This presentation provides a clear overview of how Oracle Database In-Memory optimizes both analytics and mixed workloads, delivering outstanding performance while supporting real-time analytics, business intelligence, and reporting. It provides details on what you can expect from Database In-Memory in both Oracle Database 12.1.0.2 and 12.2.
Vectorized UDF: Scalable Analysis with Python and PySpark with Li JinDatabricks
Over the past few years, Python has become the default language for data scientists. Packages such as pandas, numpy, statsmodel, and scikit-learn have gained great adoption and become the mainstream toolkits. At the same time, Apache Spark has become the de facto standard in processing big data. Spark ships with a Python interface, aka PySpark, however, because Spark’s runtime is implemented on top of JVM, using PySpark with native Python library sometimes results in poor performance and usability.
In this talk, we introduce a new type of PySpark UDF designed to solve this problem – Vectorized UDF. Vectorized UDF is built on top of Apache Arrow and bring you the best of both worlds – the ability to define easy to use, high performance UDFs and scale up your analysis with Spark.
"Extended" or "Stretched" Oracle RAC has been available as a concept for a while. Oracle RAC 12c Release 2 introduces an Oracle Extended Cluster configuration, in which the cluster understands the concept of sites and extended setups. This knowledge is used to more efficiently manage "Extended Oracle RAC", whether the nodes are 0.1 mile or 10 miles apart.
The presentation was last updated on August 7th 2017 to add a reference to the new MAA White Paper: "Installing Oracle Extended Clusters on Exadata Database Machine" - http://www.oracle.com/technetwork/database/availability/maa-extclusters-installguide-3748227.pdf and to correct some minor details.
Re-imagine Data Monitoring with whylogs and SparkDatabricks
In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data.
In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.
Deep Dive on Amazon Aurora with PostgreSQL Compatibility (DAT305-R1) - AWS re...Amazon Web Services
Amazon Aurora with PostgreSQL Compatibility is a relational database service that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. We review the functionality in order to understand the architectural differences that contribute to improved scalability, availability, and durability. We also dive deep into the capabilities of the service and review the latest available features. Finally, we walk through the techniques that can be used to migrate to Amazon Aurora.
Designing Apache Hudi for Incremental Processing With Vinoth Chandar and Etha...HostedbyConfluent
Designing Apache Hudi for Incremental Processing With Vinoth Chandar and Ethan Guo | Current 2022
Back in 2016, Apache Hudi brought transactions, change capture on top of data lakes, what is today referred to as the Lakehouse architecture. In this session, we first introduce Apache Hudi and the key technology gaps it fills in the modern data architecture. Bridging traditional data lakes and warehouses, Hudi helps realize the Lakehouse vision, by bringing transactions, optimized table metadata to data lakes and powerful storage layout optimizations, moving them closer to cloud warehouses of today. Viewed from a data engineering lens, Hudi also plays a key unifying role between the batch and stream processing worlds, by acting as a columnar, server-less ""state store"" for batch jobs, ushering in what we call the incremental processing model, where batch jobs can consume new data, update/delete intermediate results in a Hudi table, instead of re-computing/re-write entire output like old-school big batch jobs.
Rest of talk focusses on a deep dive into the some of the time-tested design choices and tradeoffs in Hudi, that helps power some of the largest transactional data lakes on the planet today. We will start by describing a tour of the storage format design, including data, metadata layouts and of course Hudi's timeline, an event log that is central to implementing ACID transactions and concurrency control. We will delve deeper into the practical concurrency control pitfalls in data lakes, and show how Hudi's hybrid approach combining MVCC with optimistic concurrency control, lowers contention and unlocks minute-level near real-time commits to Hudi tables. We will conclude with code examples that showcase Hudi's rich set of table services that perform vital table management such as cleaning older file versions, compaction of delta logs into base files, dynamic re-clustering for faster query performance, or the more recently introduced indexing service that maintains Hudi's multi-modal indexing capabilities.
"It can always get worse!" – Lessons Learned in over 20 years working with Or...Markus Michalewicz
First presented during the DOAG 2022 Conference and Exhibition, this presentation discusses and reviews the most significant lessons learned in over 20 years of working with Oracle Maximum Availability Architecture. It explains why documentation is good, but automated checks are better, and why standardization can help increase the availability of nearly all systems, including database systems.
Automating a PostgreSQL High Availability Architecture with AnsibleEDB
Highly available databases are essential to organizations depending on mission-critical, 24/7 access to data. Postgres is widely recognized as an excellent open-source database, with critical maturity and features that allow organizations to scale and achieve high availability.
EDB reference architectures are designed to help new and existing users alike to quickly design a deployment architecture that suits their needs. Users can use these reference architectures as a blueprint or as the basis for a design that enhances and extends the functionality and features offered.
This webinar will explore:
- Concepts of High Availability
- Quick review of EDB reference architectures
- EDB tools to create a highly available PostgreSQL architecture
- Options for automating the deployment of reference architectures
- EDB Ansible® roles helping in automating the deployment of reference architectures
- Features and capabilities of Ansible roles
- Automating the provisioning of the resources in the cloud using Terraform™
How to build a streaming Lakehouse with Flink, Kafka, and HudiFlink Forward
Flink Forward San Francisco 2022.
With a real-time processing engine like Flink and a transactional storage layer like Hudi, it has never been easier to build end-to-end low-latency data platforms connecting sources like Kafka to data lake storage. Come learn how to blend Lakehouse architectural patterns with real-time processing pipelines with Flink and Hudi. We will dive deep on how Flink can leverage the newest features of Hudi like multi-modal indexing that dramatically improves query and write performance, data skipping that reduces the query latency by 10x for large datasets, and many more innovations unique to Flink and Hudi.
by
Ethan Guo & Kyle Weller
Migrating a data intensive microservice from Python to GoNikolay Stoitsev
As Uber is hyper-growing as a company so does our need for scalable and resilient systems. In this talk, I’m going to tell the story of how my team migrated from Python to Go, a microservice that processes millions of events every day. First, we are going to start with the rationale behind the migration. Then we are going to go over the Python and Go tech stacks that we use. Last but not least, I’m also going to share our approach for migrating the service while running in production, adding new features and making sure there are no regressions.
This is the user manual of Launch X431 CReader VII+
>> READ MORE: https://www.obdadvisor.com/launch-creader-vii-plus-review/
Here is a detailed review of the scan tool based on my own experience, including:
- Compatibility
- Design and Specification
- Features and Functions
- Pros and Cons
Check it out to get the REVIEW and some NOTES about using this scanner.
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
ER(Entity Relationship) Diagram for online shopping - TAEHimani415946
https://bit.ly/3KACoyV
The ER diagram for the project is the foundation for the building of the database of the project. The properties, datatypes, and attributes are defined by the ER diagram.
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!