This document provides an overview and agenda for a training on Kanban change management focusing on the transfer of intermediate phases from PGI to PEA. It discusses defining a Kanban loop which includes creating material master data, a supply area, the Kanban loop, and selecting a replenishment strategy of stock transfer for the intermediate transfer between systems. The training will also cover executing, monitoring, and returning flows for the Kanban loop.
Comparative Evaluation of Spark and Flink Stream Processing Ehab Qadah
This document compares the performance of Apache Spark and Apache Flink for stream processing tasks. It develops two evaluation workloads using aircraft trajectory data from the DatAcron project: 1) computing statistics for each trajectory, and 2) detecting changes in aircraft sectors. The document finds that Flink has lower latency than Spark Streaming due to its stateful, low-latency processing model. However, Spark Streaming can achieve higher throughput than Flink by increasing batch durations. Overall, the document concludes that Flink is generally better suited than Spark for stream processing workloads.
Data Con LA 2019 - Unifying streaming and message queue with Apache Kafka by ...Data Con LA
In distributed systems, retries are inevitable. From network errors to replication issues and even outages in downstream dependencies, services operating at a massive scale must be prepared to encounter, identify, and handle failure as gracefully as possible.Given the scope and pace at which Uber operates, our systems must be fault-tolerant and uncompromising when it comes to failing intelligently. In particular, in streaming processing and event driven architecture, supporting reliable redeliveries with dead letter queues is a popular ask from many real-time applications and services at Uber.To accomplish this, we leverage Apache Kafka, a popular open source distributed pub/sub messaging platform, which has been industry-tested for delivering high performance at scale. We build competing consumption semantics with dead letter queues on top of existing Kafka APIs and provide interfaces to ack or nack out of order messages with retries and in-process fanout features. We will also talk a bit about several use cases around that such as driver/rider matching, driver incentive payment etc.
Absoft sap oil & gas supply chain forum feb 2012Absoft Limited
The document summarizes presentations at an upcoming Supply Chain Forum on returns, repairs, and rentals (RRR). It will include an overview of key RRR issues in oil and gas, the SAP solution for RRR, and demonstrations of using SAP for a maintenance work order involving returns and rentals and a drilling project involving returns, temporary storage, and rentals.
The runtime environment handles the implementation of programming language abstractions on the target machine. It allocates storage for code and data and handles access to variables, procedure linkage and parameter passing. Storage is typically divided into code, static, heap and stack areas. The compiler generates code that maps logical addresses to physical addresses. The stack grows downward and stores activation records for procedures, while the heap grows upward and dynamically allocates memory. Procedure activations are represented by activation records on the call stack. The runtime environment implements variable scoping and access to non-local data using techniques like static scopes, access links and displays.
Global available-to-promise-sap-library-sap-advanced-planner-and-optimizer-sa...Muralikrishna Kommineni
Global Available-to-Promise allows you to:
1. Check product availability across multiple systems like SAP R/3 and APO by combining basic availability methods.
2. Display alerts if shortages are found and propose alternative products or locations.
3. Create planned production orders in APO if materials are unavailable to fulfill a sales order in the R/3 system.
This document provides information about managing storage capacity using the HP 3PAR StoreServ Management Console (SSMC). It discusses key concepts such as how space is allocated to chunklets, logical disks, virtual volumes, and virtual logical units. It also describes various SSMC reports that can be used to monitor space allocation and efficiency at the system, physical drive, virtual volume, and CPG (consistent provisioning group) levels. These include the System Capacity, Physical Drives Capacity, VV-Capacity, and CPG-Space reports.
In this presentation , I have covered WM-PP interface. The presentation gives an overview of the concept as well as the process flow with and without the interface.It also talks about the master data and the various parameters that play a key role in the WM-PP processes
Aioug ha day oct2015 goldengate- High Availability Day 2015aioughydchapter
This document provides an overview of Oracle GoldenGate and discusses its key components and topologies. It begins with background information about the presenter and then covers topics such as Oracle GoldenGate's supported platforms, common topologies used with Oracle GoldenGate including unidirectional data integration and high availability, and the benefits it provides such as zero downtime upgrades and live reporting. It also discusses Oracle GoldenGate's components including the extract, replicat, trail files, and pump. Finally, it touches on performance tuning techniques for Oracle GoldenGate including adjusting TCP buffer sizes and using checkpoints.
Comparative Evaluation of Spark and Flink Stream Processing Ehab Qadah
This document compares the performance of Apache Spark and Apache Flink for stream processing tasks. It develops two evaluation workloads using aircraft trajectory data from the DatAcron project: 1) computing statistics for each trajectory, and 2) detecting changes in aircraft sectors. The document finds that Flink has lower latency than Spark Streaming due to its stateful, low-latency processing model. However, Spark Streaming can achieve higher throughput than Flink by increasing batch durations. Overall, the document concludes that Flink is generally better suited than Spark for stream processing workloads.
Data Con LA 2019 - Unifying streaming and message queue with Apache Kafka by ...Data Con LA
In distributed systems, retries are inevitable. From network errors to replication issues and even outages in downstream dependencies, services operating at a massive scale must be prepared to encounter, identify, and handle failure as gracefully as possible.Given the scope and pace at which Uber operates, our systems must be fault-tolerant and uncompromising when it comes to failing intelligently. In particular, in streaming processing and event driven architecture, supporting reliable redeliveries with dead letter queues is a popular ask from many real-time applications and services at Uber.To accomplish this, we leverage Apache Kafka, a popular open source distributed pub/sub messaging platform, which has been industry-tested for delivering high performance at scale. We build competing consumption semantics with dead letter queues on top of existing Kafka APIs and provide interfaces to ack or nack out of order messages with retries and in-process fanout features. We will also talk a bit about several use cases around that such as driver/rider matching, driver incentive payment etc.
Absoft sap oil & gas supply chain forum feb 2012Absoft Limited
The document summarizes presentations at an upcoming Supply Chain Forum on returns, repairs, and rentals (RRR). It will include an overview of key RRR issues in oil and gas, the SAP solution for RRR, and demonstrations of using SAP for a maintenance work order involving returns and rentals and a drilling project involving returns, temporary storage, and rentals.
The runtime environment handles the implementation of programming language abstractions on the target machine. It allocates storage for code and data and handles access to variables, procedure linkage and parameter passing. Storage is typically divided into code, static, heap and stack areas. The compiler generates code that maps logical addresses to physical addresses. The stack grows downward and stores activation records for procedures, while the heap grows upward and dynamically allocates memory. Procedure activations are represented by activation records on the call stack. The runtime environment implements variable scoping and access to non-local data using techniques like static scopes, access links and displays.
Global available-to-promise-sap-library-sap-advanced-planner-and-optimizer-sa...Muralikrishna Kommineni
Global Available-to-Promise allows you to:
1. Check product availability across multiple systems like SAP R/3 and APO by combining basic availability methods.
2. Display alerts if shortages are found and propose alternative products or locations.
3. Create planned production orders in APO if materials are unavailable to fulfill a sales order in the R/3 system.
This document provides information about managing storage capacity using the HP 3PAR StoreServ Management Console (SSMC). It discusses key concepts such as how space is allocated to chunklets, logical disks, virtual volumes, and virtual logical units. It also describes various SSMC reports that can be used to monitor space allocation and efficiency at the system, physical drive, virtual volume, and CPG (consistent provisioning group) levels. These include the System Capacity, Physical Drives Capacity, VV-Capacity, and CPG-Space reports.
In this presentation , I have covered WM-PP interface. The presentation gives an overview of the concept as well as the process flow with and without the interface.It also talks about the master data and the various parameters that play a key role in the WM-PP processes
Aioug ha day oct2015 goldengate- High Availability Day 2015aioughydchapter
This document provides an overview of Oracle GoldenGate and discusses its key components and topologies. It begins with background information about the presenter and then covers topics such as Oracle GoldenGate's supported platforms, common topologies used with Oracle GoldenGate including unidirectional data integration and high availability, and the benefits it provides such as zero downtime upgrades and live reporting. It also discusses Oracle GoldenGate's components including the extract, replicat, trail files, and pump. Finally, it touches on performance tuning techniques for Oracle GoldenGate including adjusting TCP buffer sizes and using checkpoints.
This document discusses Oracle Stream Analytics, which provides complex event processing capabilities for Apache Spark Streaming. It leverages Oracle's Continuous Query Engine for event-by-event processing and Apache Spark for distributed computing and fault tolerance. Key features highlighted include stateful and continuous query processing, flexible temporal windows, pattern detection, spatial analysis, and integrated business rules. It is described as reducing application development time by handling state management and fault tolerance, while also scaling linearly with Spark and providing automatic recovery from failures.
This document provides an overview of production supply methods in SAP Extended Warehouse Management (EWM). It describes key concepts like production supply areas and material staging methods. Examples are provided of pick parts staging, release order parts staging, crate parts replenishment, and production supply with kanban to illustrate how materials are staged from main storage to production areas using different transactions and document flows in SAP EWM and ERP.
The document provides an overview of the key phases and activities involved in migrating to a new general ledger (GL) using the SAP Migration Cockpit. It describes setting up a migration project with packages for different migration scenarios. The main phases discussed are setup, checkup, preparation, migration of prior and current year documents, evaluation, and validation before activation of the new GL goes live. Each phase involves various configuration checks and migration activities like creating worklists, migrating open items and documents, and validating the new financial data.
When Streaming Needs Batch With Konstantin Knauf | Current 2022HostedbyConfluent
When Streaming Needs Batch With Konstantin Knauf | Current 2022
A streaming application is started once and then continuously ingests endless, fairly steady streams of events. That's as far as the theory goes.
Unfortunately, reality is more complicated. Over time your application's ability to process large historical data sets robustly, efficiently and correctly will be critical:
- for exploratory data analysis during development
- for bootstrapping the initial state of an application
- for back-filling following an outage or bugfix
- for keeping up with bursty input streams
These scenarios call for batch processing techniques. Apache Flink is as streaming-first as it gets. Yet over the last releases, the community has invested significant resources into unifying stream- and batch processing on all layers of the stack: scheduler to APIs.
In this talk, I'll introduce Apache Flink's approach to unified stream and batch processing and discuss - by example - how these scenarios can already be addressed today and what might be possible in the future.
Final ams power_point_slides-------newwwwvivekmsmech
This document summarizes the implementation of lean manufacturing techniques in the cab trim process of a manufacturing company. Current issues like uneven work distribution, waiting times, and potential for sub-assembly were identified. Lean tools like value stream mapping and line balancing were used to propose a future state with reduced stages and cycle time. Non-value adding activities were eliminated and the line efficiency was estimated to increase from 67.1% to 88.74% through balancing workloads and incorporating sub-assembly.
Flink Forward SF 2017: Stephan Ewen - Experiences running Flink at Very Large...Flink Forward
This talk shares experiences from deploying and tuning Flink steam processing applications for very large scale. We share lessons learned from users, contributors, and our own experiments about running demanding streaming jobs at scale. The talk will explain what aspects currently render a job as particularly demanding, show how to configure and tune a large scale Flink job, and outline what the Flink community is working on to make the out-of-the-box for experience as smooth as possible. We will, for example, dive into - analyzing and tuning checkpointing - selecting and configuring state backends - understanding common bottlenecks - understanding and configuring network parameters
The document discusses SAP's Plant Maintenance (PM) module. It describes how PM provides planning, control, and processing of scheduled maintenance, inspection, damage-related maintenance, and service management to ensure availability of operational systems. The document also outlines procedures for asset and activity registration, maintenance plan creation, and function location in SAP PM. It provides details on uploading methods and templates for loading new project data into SAP PM.
The document discusses administering parallel execution in Oracle databases. It describes how parallel query uses slave processes to perform work across instances, and how the placement of slaves can be controlled using services or parallel instance groups. It provides an example execution plan showing how slaves perform different tasks like scanning and sorting. It also covers best practices, new features in Oracle 11g like parallel statement queueing, and how parallel DML works.
DevoxxUK: Optimizating Application Performance on KubernetesDinakar Guniguntala
Now that you have your apps running on K8s, wondering how to get the response time that you need ? Tuning a polyglot set of microservices to get the performance that you need can be challenging in Kubernetes. The key to overcoming this is observability. Luckily there are a number of tools such as Prometheus that can provide all the metrics you need, but here is the catch, there is so much of data and metrics that is difficult make sense of it all. This is where Hyperparameter tuning can come to the rescue to help build the right models.
This talk covers best practices that will help attendees
1. To understand and avoid common performance related problems.
2. Discuss observability tools and how they can help identify perf issues.
3. Look closer into Kruize Autotune which is a Open Source Autonomous Performance Tuning Tool for Kubernetes and where it can help.
This document provides an overview of handling unit management in SAP. It defines what a handling unit is and explains how handling units can be used to simplify logistics processes. It also describes the key customizing settings needed to implement handling unit management, such as defining packaging materials and number assignment. The document outlines how handling units can be identified and tracked across business processes.
This document provides steps for LO extraction from SAP R/3 to SAP BI/BW. It discusses LBWE activities like scheduling extraction jobs, different update modes including direct delta, queued delta, and unserialized V3 update. It also covers setup tables which collect required data from application tables to initialize delta loads and full loads, avoiding direct access to R/3 tables. The author is an SAP BI consultant who has experience with ABAP, custom report development, and system migrations.
The webinar covered new features and updates to the Nephele 2.0 bioinformatics analysis platform. Key updates included a new website interface, improved performance through a new infrastructure framework, the ability to resubmit jobs by ID, and interactive mapping file submission. New pipelines for 16S analysis using DADA2 and quality control preprocessing were introduced, and the existing 16S mothur pipeline was updated. The quality control pipeline provides tools to assess data quality before running microbiome analyses through FastQC, primer/adapter trimming with cutadapt, and additional quality filtering options. The webinar emphasized the importance of data quality checks and highlighted troubleshooting tips such as examining the log file for error messages when jobs fail.
Standard SAP ECC 6.0 functionality does not provide an ability to assign a Pick and Drop Storage for VNA Storage or any High Rack Storage.
There now exists a solution that alleviates the aforesaid GAP in SAP ECC 6.0.
For more details on the solution, please see attached solution brief.
This document provides an overview of Oracle Stream Analytics capabilities for processing fast streaming data. It discusses deployment approaches on Oracle Cloud, hybrid cloud, and on-premises. It also covers event processing techniques like pattern detection, time windows, and continuous querying enabled by Oracle Stream Analytics. Specific use cases for retail and healthcare are also presented.
Tao Feng gave a presentation on Airflow at Lyft. Some key points:
1) Lyft uses Apache Airflow for ETL workflows with over 600 DAGs and 800 DAG runs daily across three AWS Auto Scaling Groups of worker nodes.
2) Lyft has customized Airflow with additional UI links, DAG dependency graphs, and integration with internal tools.
3) Lyft is working to improve the backfill experience, support DAG-level access controls, and explore running Airflow with Kubernetes executors.
4) Tao discussed challenges like daylight saving time issues and long-running tasks occupying slots, and thanked other Lyft engineers contributing to Airflow.
The document defines key terms related to production planning and execution in SAP, including material requirements planning (MRP), capacity requirements planning (CRP), process orders, and batch production records (BPR). It then provides an overview of the production preparation and execution processes in SAP, outlining activities like analyzing MRP results, capacity leveling, scheduling planned orders, and converting them to process orders. Process execution steps are also summarized, such as releasing process orders, confirming operations, conducting quality inspections, and goods receipt.
Resilient Predictive Data Pipelines (QCon London 2016)Sid Anand
This document discusses building resilient predictive data pipelines. It begins by distinguishing between ETL and predictive data pipelines, noting that predictive pipelines require high availability with downtimes of less than an hour. The document then outlines design goals for resilient data pipelines, including being scalable, available, instrumented/monitored/alert-enabled, and quickly recoverable. It proposes using AWS services like SQS, SNS, S3, and Auto Scaling Groups to build such pipelines. The document also recommends using Apache Airflow for workflow automation and scheduling to reliably manage pipelines as directed acyclic graphs. It presents an architecture using these techniques and assesses how well it meets the outlined design goals.
This document provides an overview and introduction to demand flow technology (DFT) principles and techniques for lean manufacturing. It discusses key DFT concepts like total product cycle time, takt time, line balancing, kanban systems, mixed model production, and measuring production linearity. The objectives are to develop knowledge of DFT philosophies and techniques, understand the total business strategy, and learn how to establish and perform the skills like setting operational standards and synchronizing production processes.
Presentation Compliance Software Events And Internet Enfoeppie
This document describes various software solutions from Rescop B.V. for regulatory compliance of automated systems. It outlines the objectives and key functionality of their RC-DMS document management system, RC-CAPA deviation handling and corrective action system, RC-CMDB asset and configuration management database, RC-Logbook scheduling and logging system, RC-VMP validation master planning system, RC-SDLC system for validation, qualification and commissioning, and their RC-ECMS enterprise compliance management suite for integrating multiple systems. The solutions are designed to help life sciences companies comply with Good Manufacturing Practices regulations.
This document discusses Oracle Stream Analytics, which provides complex event processing capabilities for Apache Spark Streaming. It leverages Oracle's Continuous Query Engine for event-by-event processing and Apache Spark for distributed computing and fault tolerance. Key features highlighted include stateful and continuous query processing, flexible temporal windows, pattern detection, spatial analysis, and integrated business rules. It is described as reducing application development time by handling state management and fault tolerance, while also scaling linearly with Spark and providing automatic recovery from failures.
This document provides an overview of production supply methods in SAP Extended Warehouse Management (EWM). It describes key concepts like production supply areas and material staging methods. Examples are provided of pick parts staging, release order parts staging, crate parts replenishment, and production supply with kanban to illustrate how materials are staged from main storage to production areas using different transactions and document flows in SAP EWM and ERP.
The document provides an overview of the key phases and activities involved in migrating to a new general ledger (GL) using the SAP Migration Cockpit. It describes setting up a migration project with packages for different migration scenarios. The main phases discussed are setup, checkup, preparation, migration of prior and current year documents, evaluation, and validation before activation of the new GL goes live. Each phase involves various configuration checks and migration activities like creating worklists, migrating open items and documents, and validating the new financial data.
When Streaming Needs Batch With Konstantin Knauf | Current 2022HostedbyConfluent
When Streaming Needs Batch With Konstantin Knauf | Current 2022
A streaming application is started once and then continuously ingests endless, fairly steady streams of events. That's as far as the theory goes.
Unfortunately, reality is more complicated. Over time your application's ability to process large historical data sets robustly, efficiently and correctly will be critical:
- for exploratory data analysis during development
- for bootstrapping the initial state of an application
- for back-filling following an outage or bugfix
- for keeping up with bursty input streams
These scenarios call for batch processing techniques. Apache Flink is as streaming-first as it gets. Yet over the last releases, the community has invested significant resources into unifying stream- and batch processing on all layers of the stack: scheduler to APIs.
In this talk, I'll introduce Apache Flink's approach to unified stream and batch processing and discuss - by example - how these scenarios can already be addressed today and what might be possible in the future.
Final ams power_point_slides-------newwwwvivekmsmech
This document summarizes the implementation of lean manufacturing techniques in the cab trim process of a manufacturing company. Current issues like uneven work distribution, waiting times, and potential for sub-assembly were identified. Lean tools like value stream mapping and line balancing were used to propose a future state with reduced stages and cycle time. Non-value adding activities were eliminated and the line efficiency was estimated to increase from 67.1% to 88.74% through balancing workloads and incorporating sub-assembly.
Flink Forward SF 2017: Stephan Ewen - Experiences running Flink at Very Large...Flink Forward
This talk shares experiences from deploying and tuning Flink steam processing applications for very large scale. We share lessons learned from users, contributors, and our own experiments about running demanding streaming jobs at scale. The talk will explain what aspects currently render a job as particularly demanding, show how to configure and tune a large scale Flink job, and outline what the Flink community is working on to make the out-of-the-box for experience as smooth as possible. We will, for example, dive into - analyzing and tuning checkpointing - selecting and configuring state backends - understanding common bottlenecks - understanding and configuring network parameters
The document discusses SAP's Plant Maintenance (PM) module. It describes how PM provides planning, control, and processing of scheduled maintenance, inspection, damage-related maintenance, and service management to ensure availability of operational systems. The document also outlines procedures for asset and activity registration, maintenance plan creation, and function location in SAP PM. It provides details on uploading methods and templates for loading new project data into SAP PM.
The document discusses administering parallel execution in Oracle databases. It describes how parallel query uses slave processes to perform work across instances, and how the placement of slaves can be controlled using services or parallel instance groups. It provides an example execution plan showing how slaves perform different tasks like scanning and sorting. It also covers best practices, new features in Oracle 11g like parallel statement queueing, and how parallel DML works.
DevoxxUK: Optimizating Application Performance on KubernetesDinakar Guniguntala
Now that you have your apps running on K8s, wondering how to get the response time that you need ? Tuning a polyglot set of microservices to get the performance that you need can be challenging in Kubernetes. The key to overcoming this is observability. Luckily there are a number of tools such as Prometheus that can provide all the metrics you need, but here is the catch, there is so much of data and metrics that is difficult make sense of it all. This is where Hyperparameter tuning can come to the rescue to help build the right models.
This talk covers best practices that will help attendees
1. To understand and avoid common performance related problems.
2. Discuss observability tools and how they can help identify perf issues.
3. Look closer into Kruize Autotune which is a Open Source Autonomous Performance Tuning Tool for Kubernetes and where it can help.
This document provides an overview of handling unit management in SAP. It defines what a handling unit is and explains how handling units can be used to simplify logistics processes. It also describes the key customizing settings needed to implement handling unit management, such as defining packaging materials and number assignment. The document outlines how handling units can be identified and tracked across business processes.
This document provides steps for LO extraction from SAP R/3 to SAP BI/BW. It discusses LBWE activities like scheduling extraction jobs, different update modes including direct delta, queued delta, and unserialized V3 update. It also covers setup tables which collect required data from application tables to initialize delta loads and full loads, avoiding direct access to R/3 tables. The author is an SAP BI consultant who has experience with ABAP, custom report development, and system migrations.
The webinar covered new features and updates to the Nephele 2.0 bioinformatics analysis platform. Key updates included a new website interface, improved performance through a new infrastructure framework, the ability to resubmit jobs by ID, and interactive mapping file submission. New pipelines for 16S analysis using DADA2 and quality control preprocessing were introduced, and the existing 16S mothur pipeline was updated. The quality control pipeline provides tools to assess data quality before running microbiome analyses through FastQC, primer/adapter trimming with cutadapt, and additional quality filtering options. The webinar emphasized the importance of data quality checks and highlighted troubleshooting tips such as examining the log file for error messages when jobs fail.
Standard SAP ECC 6.0 functionality does not provide an ability to assign a Pick and Drop Storage for VNA Storage or any High Rack Storage.
There now exists a solution that alleviates the aforesaid GAP in SAP ECC 6.0.
For more details on the solution, please see attached solution brief.
This document provides an overview of Oracle Stream Analytics capabilities for processing fast streaming data. It discusses deployment approaches on Oracle Cloud, hybrid cloud, and on-premises. It also covers event processing techniques like pattern detection, time windows, and continuous querying enabled by Oracle Stream Analytics. Specific use cases for retail and healthcare are also presented.
Tao Feng gave a presentation on Airflow at Lyft. Some key points:
1) Lyft uses Apache Airflow for ETL workflows with over 600 DAGs and 800 DAG runs daily across three AWS Auto Scaling Groups of worker nodes.
2) Lyft has customized Airflow with additional UI links, DAG dependency graphs, and integration with internal tools.
3) Lyft is working to improve the backfill experience, support DAG-level access controls, and explore running Airflow with Kubernetes executors.
4) Tao discussed challenges like daylight saving time issues and long-running tasks occupying slots, and thanked other Lyft engineers contributing to Airflow.
The document defines key terms related to production planning and execution in SAP, including material requirements planning (MRP), capacity requirements planning (CRP), process orders, and batch production records (BPR). It then provides an overview of the production preparation and execution processes in SAP, outlining activities like analyzing MRP results, capacity leveling, scheduling planned orders, and converting them to process orders. Process execution steps are also summarized, such as releasing process orders, confirming operations, conducting quality inspections, and goods receipt.
Resilient Predictive Data Pipelines (QCon London 2016)Sid Anand
This document discusses building resilient predictive data pipelines. It begins by distinguishing between ETL and predictive data pipelines, noting that predictive pipelines require high availability with downtimes of less than an hour. The document then outlines design goals for resilient data pipelines, including being scalable, available, instrumented/monitored/alert-enabled, and quickly recoverable. It proposes using AWS services like SQS, SNS, S3, and Auto Scaling Groups to build such pipelines. The document also recommends using Apache Airflow for workflow automation and scheduling to reliably manage pipelines as directed acyclic graphs. It presents an architecture using these techniques and assesses how well it meets the outlined design goals.
This document provides an overview and introduction to demand flow technology (DFT) principles and techniques for lean manufacturing. It discusses key DFT concepts like total product cycle time, takt time, line balancing, kanban systems, mixed model production, and measuring production linearity. The objectives are to develop knowledge of DFT philosophies and techniques, understand the total business strategy, and learn how to establish and perform the skills like setting operational standards and synchronizing production processes.
Presentation Compliance Software Events And Internet Enfoeppie
This document describes various software solutions from Rescop B.V. for regulatory compliance of automated systems. It outlines the objectives and key functionality of their RC-DMS document management system, RC-CAPA deviation handling and corrective action system, RC-CMDB asset and configuration management database, RC-Logbook scheduling and logging system, RC-VMP validation master planning system, RC-SDLC system for validation, qualification and commissioning, and their RC-ECMS enterprise compliance management suite for integrating multiple systems. The solutions are designed to help life sciences companies comply with Good Manufacturing Practices regulations.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/