This presentation by Think Big principal Matt Cooke and Martin Oberhuber, Senior Data Scientist, discusses high frequency trading, requirements for success, and underlying architectures which may include Apache Spark.
This presentation given by Think Big's senior data scientist Eliano Marques at Digital Natives conference in Berlin, Germany (November 2015), details how to go from experimentation to productionization for a predictive maintenance use case.
The benefits of Hadoop for analytics make it a popular option for many companies looking to expand their analytics suite. However, adding Hadoop as an analytics platform to an existing environment based on more traditional data structures and methods poses several key challenges. Review these slides to understand key challenges and strategies to expanding the analytics suite to use Hadoop, such as: architectural integration with existing platforms, skills and organizational readiness, and the importance of a vision and a clear path forward.
Accelerating Fast Data Strategy with Data VirtualizationDenodo
"Information from the past won't support the insights of the future - businesses need real-time data," said Forrester Analyst Noel Yuhanna. In this presentation, he explains the challenges of latent data faced by business users, the need to accelerate fast data strategy using data virtualization, and the implications of such strategy.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/a2xNyZ.
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...Precisely
Teams working on new business initiatives, whether for enhancing customer engagement, creating new value, or addressing compliance considerations, know that a successful strategy starts with the synchronization of operational and reporting data from across the organization into a centralized repository for use in advanced analytics and other projects. However, the range and complexity of data sources as well as the lack of specialized skills needed to extract data from critical legacy systems often causes inefficiencies and gaps in the data being used by the business.
The first part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Syncsort Connect with its design once, deploy anywhere approach supports a repeatable pattern for data integration by enabling enterprise architects and developers to ensure data from ALL enterprise data sources– from mainframe to cloud – is available in the downstream data lakes for use in these key business initiatives.
In this Strata+Hadoop World 2015 presentation, Ron Bodkin, President of Think Big, a Teradata company, explains changes for data modeling on big data systems and five important new analytic patterns becoming more commonplace as companies grow their data driven capabilities.
Foundational Strategies for Trust in Big Data Part 2: Understanding Your DataPrecisely
This document provides an overview of understanding data through data profiling. It discusses:
1. The five key steps to effective data profiling: defining how to analyze the data, what to review, what to look for, when to build rules, and what to communicate.
2. Common challenges with big data and new data types, and measurements for assessing data quality.
3. A case study of how British Airways leveraged data profiling and governance to ensure accurate customer data across multiple systems and improve analysis, marketing and service.
Data Science in Action for an Insurance Product - Shawn JinMolly Alexander
This document summarizes how an insurance company uses data science across its operations, from marketing to pricing to claims processing. Data from various sources is used to separate good and bad risks and test analytical models. Data scientists work closely with product teams to develop new strategies and continuously improve the product. A standard analytical process is enforced to institutionalize best practices. The company also builds a platform for individuals to develop technical skills and advance their careers in data science.
This presentation given by Think Big's senior data scientist Eliano Marques at Digital Natives conference in Berlin, Germany (November 2015), details how to go from experimentation to productionization for a predictive maintenance use case.
The benefits of Hadoop for analytics make it a popular option for many companies looking to expand their analytics suite. However, adding Hadoop as an analytics platform to an existing environment based on more traditional data structures and methods poses several key challenges. Review these slides to understand key challenges and strategies to expanding the analytics suite to use Hadoop, such as: architectural integration with existing platforms, skills and organizational readiness, and the importance of a vision and a clear path forward.
Accelerating Fast Data Strategy with Data VirtualizationDenodo
"Information from the past won't support the insights of the future - businesses need real-time data," said Forrester Analyst Noel Yuhanna. In this presentation, he explains the challenges of latent data faced by business users, the need to accelerate fast data strategy using data virtualization, and the implications of such strategy.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/a2xNyZ.
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...Precisely
Teams working on new business initiatives, whether for enhancing customer engagement, creating new value, or addressing compliance considerations, know that a successful strategy starts with the synchronization of operational and reporting data from across the organization into a centralized repository for use in advanced analytics and other projects. However, the range and complexity of data sources as well as the lack of specialized skills needed to extract data from critical legacy systems often causes inefficiencies and gaps in the data being used by the business.
The first part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Syncsort Connect with its design once, deploy anywhere approach supports a repeatable pattern for data integration by enabling enterprise architects and developers to ensure data from ALL enterprise data sources– from mainframe to cloud – is available in the downstream data lakes for use in these key business initiatives.
In this Strata+Hadoop World 2015 presentation, Ron Bodkin, President of Think Big, a Teradata company, explains changes for data modeling on big data systems and five important new analytic patterns becoming more commonplace as companies grow their data driven capabilities.
Foundational Strategies for Trust in Big Data Part 2: Understanding Your DataPrecisely
This document provides an overview of understanding data through data profiling. It discusses:
1. The five key steps to effective data profiling: defining how to analyze the data, what to review, what to look for, when to build rules, and what to communicate.
2. Common challenges with big data and new data types, and measurements for assessing data quality.
3. A case study of how British Airways leveraged data profiling and governance to ensure accurate customer data across multiple systems and improve analysis, marketing and service.
Data Science in Action for an Insurance Product - Shawn JinMolly Alexander
This document summarizes how an insurance company uses data science across its operations, from marketing to pricing to claims processing. Data from various sources is used to separate good and bad risks and test analytical models. Data scientists work closely with product teams to develop new strategies and continuously improve the product. A standard analytical process is enforced to institutionalize best practices. The company also builds a platform for individuals to develop technical skills and advance their careers in data science.
From Foundation to Mastery – Building a Mature Analytics Roadmap - Manav MisraMolly Alexander
The document outlines steps to build a mature analytics roadmap for a financial services organization. It discusses:
1) Establishing a leadership team to create an analytics strategy and bridge business needs with data solutions.
2) Developing data products that use analytics to provide value and insights to end users.
3) Implementing a modern data science platform to manage data, run analytics, and deploy models at scale.
4) Implementing data management practices like a data catalog and data lake to break down silos and ensure governance.
5) Fostering a data-driven culture with executive sponsorship of data products and integration with business units.
Maximizing The Value of Your Structured and Unstructured Data with Data Catal...Molly Alexander
The document discusses how data catalogs can be used to extract value from both structured and unstructured data by providing context about distributed data assets to enable various roles like data scientists and analysts to find and understand relevant datasets, and it recommends implementing an augmented data catalog using machine learning to automatically curate, verify and classify data to improve data quality and insights over time. The document also provides an overview of how to implement a phased data governance approach using a data catalog.
Ensuring Data Quality and Lineage in Cloud Migration - Dan PowerMolly Alexander
Dan Power, Managing Director and Head of Data Governance at State Street Global Markets, gave a presentation on ensuring data quality and lineage when migrating to the cloud. He discussed how moving to the cloud presents both benefits like scalability and cost savings, but also challenges for maintaining data quality. Power recommended using the cloud migration as an opportunity to strengthen data governance strategies and automate quality checks. He also emphasized the importance of building collaborative frameworks between analytics, data, and governance teams to optimize how data is managed and used across cloud environments.
1) The document discusses how businesses can extract value from data by transforming it into useful insights and applying those insights. 2) It provides examples of the types of data that can be collected from customers (transactions, website visits, searches) and the insights that can be derived (customer types, purchase propensities). 3) Finally, it discusses how businesses can apply those insights to generate value through targeted marketing, promotions, and other business solutions that increase revenue, lower costs, and improve productivity.
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to come to grips with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives as other organizational assets do. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately however, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic OM plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers.
Check out more of our webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
The Shifting Landscape of Data IntegrationDATAVERSITY
This document discusses the shifting landscape of data integration. It begins with an introduction by William McKnight, who is described as the "#1 Global Influencer in Data Warehousing". The document then discusses how challenges in data integration are shifting from dealing with volume, velocity and variety to dealing with dynamic, distributed and diverse data in the cloud. It also discusses IDC's view that this shift is occurring from the traditional 3Vs to the 3Ds. The rest of the document discusses Matillion, a vendor that provides a modern solution for cloud data integration challenges.
Best Practices: Datawarehouse Automation Conference September 20, 2012 - Amst...Erik Fransen
The document discusses best practices for data warehouse automation. It covers challenges organizations face with business intelligence (BI), how data warehouse (DWH) automation can help address these challenges, and the Centennium BI Ability Model for DWH automation. Case studies of successful DWH automation projects at Rotterdam and KAS BANK are provided. The presentation also outlines the Centennium Methodology (CDM) for DWH automation best practices and concludes with information about Centennium as an independent BI expertise organization.
This is the third in our three part webinar series on cloud-enabled customer insights. Learn how to scale your customer analytics operations up and out with Microsoft Azure Data Lake.
Too often I hear the question “Can you help me with our Data Strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component – the Data Strategy itself. A more useful request is this: “Can you help me apply data strategically?”Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) Data Strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” Refocus on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. This approach can also contribute to three primary organizational data goals.
In this webinar, you will learn how improving your organization’s data, the way your people use data, and the way your people use data to achieve your organizational strategy will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs, as organizations identify prioritized areas where better assets, literacy, and support (Data Strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why Data Strategy is necessary for effective Data Governance
- An overview of prerequisites for effective strategic use of Data Strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
NLB is a technology, analytics and advisory services company founded in 2007 with over 1500 resources in the US and Canada. It helps clients innovate and improve processes through best practices from Fortune 1000 companies. NLB provides services including analytics, process re-engineering, staff augmentation, and vendor environment optimization to help clients reduce costs and realize measurable business impacts. It takes an end-to-end approach to predictive analytics from identifying patterns to developing prescriptive actions and embedding insights into client systems and culture.
This document provides an overview and strategy for big and fast data initiatives in 2017. It discusses the data landscape including volume, velocity, variety and validity. It evaluates different data platform technologies and outlines requirements. The vision is described as "Business Insights at the Speed of Light". The strategy focuses on speed and leveraging key technologies like Spark. A roadmap with initiatives around insights, infrastructure, ingestion and big BI is presented. High level architectures for streaming and data flow are shown. Finally, data preparation vendors are compared.
This document provides an overview of big data and its applications. It discusses the three Vs of big data - volume, velocity, and variety. It also defines big data and outlines some key areas related to big data like technology, analytics, and data capture/storage/management. Trends showing increasing interest in both customer experience and big data are presented. Five high value use cases of big data for businesses are outlined. The document also discusses what customers and vendors think are important aspects of big data. Studies finding relationships between analytics use and business performance as well as the importance of data integration are referenced. Overall, the document presents a high-level introduction to big data concepts, applications, and considerations.
Scott Fairbanks, Senior BI Consultant at CCG, demonstrates the key differentiators between traditional warehouse architectures and new cloud technologies. Learn the key competitors in the cloud space, and what elements separates them in terms of linking analytic solutions.
Hiring and Developing Analytics Talent in the CPG and Retail Industry - Mohi...Molly Alexander
1. The document discusses how to hire and retain analytics talent in the consumer packaged goods industry. It emphasizes the need for strong analytics leadership to develop a clear talent strategy and define analytics roles.
2. It highlights the importance of "analytic translators" who can communicate between business and technical teams to identify high-impact use cases. It also stresses prioritizing analytic workstreams and building expertise within each.
3. The document provides examples of when to buy versus build analytics capabilities and outlines what data scientists, engineers, and visualizers want in their roles to aid retention. It emphasizes delivering on promises and a culture of innovation.
Virtual Governance in a Time of Crisis WorkshopCCG
The CCGDG framework is focused on the following 5 key competencies. These 5 competencies were identified as areas within DG that have the biggest ROI for you, our customer. The pandemic has uncovered many challenges related to governance, therefore the backbone of this model is the emphasis on risk mitigation.
1. Program Management
2. Data Quality
3. Data Architecture
4. Metadata Management
5. Privacy
Эволюция Big Data и Information Management. Reference Architecture.Andrey Akulov
This document outlines Oracle's third generation Information Management Reference Architecture. It defines key concepts like the Raw Data Reservoir for storing immutable raw data, and the Foundation Data Layer for standardized enterprise data. It describes logical components like the Data Factory for ingestion and interpretation, and the Access and Performance Layer for enabling queries. It also provides design patterns for different use cases including a Discovery Lab, Information Platform, and Real-Time Event processing. Overall the architecture aims to practically manage all types of data at scale to maximize information value.
Modern Integrated Data Environment - Whitepaper | QuboleVasu S
A whit-paper is about building a modern data platform for data driven organisations with using cloud data warehouse with modern data platform architecture
https://www.qubole.com/resources/white-papers/modern-integrated-data-environment
Claudia Imhoff of the Boulder BI Brain Trust gives the lowdown on integrating real-time data to leverage modern BI practices for your business in this Information Builders Innovation Session presentation.
High Frequency Trading and NoSQL databasePeter Lawrey
This document discusses high frequency trading systems and the requirements and technologies used, including:
- HFT systems require extremely low latency databases (microseconds) and event-driven processing to minimize latency.
- OpenHFT provides low-latency logging and data storage technologies like Chronicle and HugeCollections for use in HFT systems.
- Chronicle provides microsecond-latency logging and replication between processes. HugeCollections provides high-throughput concurrent key-value storage with microsecond-level latencies.
- These technologies are useful for critical data in HFT systems where traditional databases cannot meet the latency and throughput requirements.
How are systems in finance design for deterministic outcomes, and performance. What are the benefits and what is the performance you can achieve.
Included a demo you can download.
From Foundation to Mastery – Building a Mature Analytics Roadmap - Manav MisraMolly Alexander
The document outlines steps to build a mature analytics roadmap for a financial services organization. It discusses:
1) Establishing a leadership team to create an analytics strategy and bridge business needs with data solutions.
2) Developing data products that use analytics to provide value and insights to end users.
3) Implementing a modern data science platform to manage data, run analytics, and deploy models at scale.
4) Implementing data management practices like a data catalog and data lake to break down silos and ensure governance.
5) Fostering a data-driven culture with executive sponsorship of data products and integration with business units.
Maximizing The Value of Your Structured and Unstructured Data with Data Catal...Molly Alexander
The document discusses how data catalogs can be used to extract value from both structured and unstructured data by providing context about distributed data assets to enable various roles like data scientists and analysts to find and understand relevant datasets, and it recommends implementing an augmented data catalog using machine learning to automatically curate, verify and classify data to improve data quality and insights over time. The document also provides an overview of how to implement a phased data governance approach using a data catalog.
Ensuring Data Quality and Lineage in Cloud Migration - Dan PowerMolly Alexander
Dan Power, Managing Director and Head of Data Governance at State Street Global Markets, gave a presentation on ensuring data quality and lineage when migrating to the cloud. He discussed how moving to the cloud presents both benefits like scalability and cost savings, but also challenges for maintaining data quality. Power recommended using the cloud migration as an opportunity to strengthen data governance strategies and automate quality checks. He also emphasized the importance of building collaborative frameworks between analytics, data, and governance teams to optimize how data is managed and used across cloud environments.
1) The document discusses how businesses can extract value from data by transforming it into useful insights and applying those insights. 2) It provides examples of the types of data that can be collected from customers (transactions, website visits, searches) and the insights that can be derived (customer types, purchase propensities). 3) Finally, it discusses how businesses can apply those insights to generate value through targeted marketing, promotions, and other business solutions that increase revenue, lower costs, and improve productivity.
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to come to grips with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives as other organizational assets do. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately however, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic OM plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers.
Check out more of our webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
The Shifting Landscape of Data IntegrationDATAVERSITY
This document discusses the shifting landscape of data integration. It begins with an introduction by William McKnight, who is described as the "#1 Global Influencer in Data Warehousing". The document then discusses how challenges in data integration are shifting from dealing with volume, velocity and variety to dealing with dynamic, distributed and diverse data in the cloud. It also discusses IDC's view that this shift is occurring from the traditional 3Vs to the 3Ds. The rest of the document discusses Matillion, a vendor that provides a modern solution for cloud data integration challenges.
Best Practices: Datawarehouse Automation Conference September 20, 2012 - Amst...Erik Fransen
The document discusses best practices for data warehouse automation. It covers challenges organizations face with business intelligence (BI), how data warehouse (DWH) automation can help address these challenges, and the Centennium BI Ability Model for DWH automation. Case studies of successful DWH automation projects at Rotterdam and KAS BANK are provided. The presentation also outlines the Centennium Methodology (CDM) for DWH automation best practices and concludes with information about Centennium as an independent BI expertise organization.
This is the third in our three part webinar series on cloud-enabled customer insights. Learn how to scale your customer analytics operations up and out with Microsoft Azure Data Lake.
Too often I hear the question “Can you help me with our Data Strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component – the Data Strategy itself. A more useful request is this: “Can you help me apply data strategically?”Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) Data Strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” Refocus on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. This approach can also contribute to three primary organizational data goals.
In this webinar, you will learn how improving your organization’s data, the way your people use data, and the way your people use data to achieve your organizational strategy will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs, as organizations identify prioritized areas where better assets, literacy, and support (Data Strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why Data Strategy is necessary for effective Data Governance
- An overview of prerequisites for effective strategic use of Data Strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
NLB is a technology, analytics and advisory services company founded in 2007 with over 1500 resources in the US and Canada. It helps clients innovate and improve processes through best practices from Fortune 1000 companies. NLB provides services including analytics, process re-engineering, staff augmentation, and vendor environment optimization to help clients reduce costs and realize measurable business impacts. It takes an end-to-end approach to predictive analytics from identifying patterns to developing prescriptive actions and embedding insights into client systems and culture.
This document provides an overview and strategy for big and fast data initiatives in 2017. It discusses the data landscape including volume, velocity, variety and validity. It evaluates different data platform technologies and outlines requirements. The vision is described as "Business Insights at the Speed of Light". The strategy focuses on speed and leveraging key technologies like Spark. A roadmap with initiatives around insights, infrastructure, ingestion and big BI is presented. High level architectures for streaming and data flow are shown. Finally, data preparation vendors are compared.
This document provides an overview of big data and its applications. It discusses the three Vs of big data - volume, velocity, and variety. It also defines big data and outlines some key areas related to big data like technology, analytics, and data capture/storage/management. Trends showing increasing interest in both customer experience and big data are presented. Five high value use cases of big data for businesses are outlined. The document also discusses what customers and vendors think are important aspects of big data. Studies finding relationships between analytics use and business performance as well as the importance of data integration are referenced. Overall, the document presents a high-level introduction to big data concepts, applications, and considerations.
Scott Fairbanks, Senior BI Consultant at CCG, demonstrates the key differentiators between traditional warehouse architectures and new cloud technologies. Learn the key competitors in the cloud space, and what elements separates them in terms of linking analytic solutions.
Hiring and Developing Analytics Talent in the CPG and Retail Industry - Mohi...Molly Alexander
1. The document discusses how to hire and retain analytics talent in the consumer packaged goods industry. It emphasizes the need for strong analytics leadership to develop a clear talent strategy and define analytics roles.
2. It highlights the importance of "analytic translators" who can communicate between business and technical teams to identify high-impact use cases. It also stresses prioritizing analytic workstreams and building expertise within each.
3. The document provides examples of when to buy versus build analytics capabilities and outlines what data scientists, engineers, and visualizers want in their roles to aid retention. It emphasizes delivering on promises and a culture of innovation.
Virtual Governance in a Time of Crisis WorkshopCCG
The CCGDG framework is focused on the following 5 key competencies. These 5 competencies were identified as areas within DG that have the biggest ROI for you, our customer. The pandemic has uncovered many challenges related to governance, therefore the backbone of this model is the emphasis on risk mitigation.
1. Program Management
2. Data Quality
3. Data Architecture
4. Metadata Management
5. Privacy
Эволюция Big Data и Information Management. Reference Architecture.Andrey Akulov
This document outlines Oracle's third generation Information Management Reference Architecture. It defines key concepts like the Raw Data Reservoir for storing immutable raw data, and the Foundation Data Layer for standardized enterprise data. It describes logical components like the Data Factory for ingestion and interpretation, and the Access and Performance Layer for enabling queries. It also provides design patterns for different use cases including a Discovery Lab, Information Platform, and Real-Time Event processing. Overall the architecture aims to practically manage all types of data at scale to maximize information value.
Modern Integrated Data Environment - Whitepaper | QuboleVasu S
A whit-paper is about building a modern data platform for data driven organisations with using cloud data warehouse with modern data platform architecture
https://www.qubole.com/resources/white-papers/modern-integrated-data-environment
Claudia Imhoff of the Boulder BI Brain Trust gives the lowdown on integrating real-time data to leverage modern BI practices for your business in this Information Builders Innovation Session presentation.
High Frequency Trading and NoSQL databasePeter Lawrey
This document discusses high frequency trading systems and the requirements and technologies used, including:
- HFT systems require extremely low latency databases (microseconds) and event-driven processing to minimize latency.
- OpenHFT provides low-latency logging and data storage technologies like Chronicle and HugeCollections for use in HFT systems.
- Chronicle provides microsecond-latency logging and replication between processes. HugeCollections provides high-throughput concurrent key-value storage with microsecond-level latencies.
- These technologies are useful for critical data in HFT systems where traditional databases cannot meet the latency and throughput requirements.
How are systems in finance design for deterministic outcomes, and performance. What are the benefits and what is the performance you can achieve.
Included a demo you can download.
Low latency microservices in java QCon New York 2016Peter Lawrey
In this talk we explore how Microservices and Trading System overlap and what they can learn from each other. In particular, how can we make microservices easy to test and performant. How can Trading System have shorter time to market and easier to maintain.
Learn how you can enjoy the developer productivity, low TCO, and unlimited scale of MongoDB as a tick database for capturing, analyzing, and taking advantage of opportunities in tick data. This presentation will illustrates how MongoDB can easily and quickly store variable data formats, like top and depth of book, multiple asset classes, and even news and social networking feeds. It will explore aggregating and analyzing tick data in real-time for automated trading or in batch for research and analysis and how auto-sharding enables MongoDB to scale with commodity hardware to satisfy unlimited storage and performance requirements.
Writing and testing high frequency trading engines in javaPeter Lawrey
JavaOne presentation of Writing and Testing High Frequency Trading Engines in Java. Talk looks at low latency trading, thread affinity, lock free code, ultra low garbage and low latency persistence and IPC.
Big data predictive analytics in trading & asset management lars hamberg Lars Hamberg
Big Data Predictive Analytics in Trading & Asset Management - slides and key take-aways from keynote speech at Big Data Day AIM/KTH Royal Institute of Technology, Stockholm
Deterministic behaviour and performance in trading systemsPeter Lawrey
Peter Lawrey gave a presentation on deterministic behavior and performance in trading. Some key points:
- Using lambda functions and state machines can help make systems more deterministic and easy to reason about.
- Recording all inputs and outputs allows systems to be replayed and upgraded deterministically. This supports testing.
- Little's Law relates throughput, latency, and number of workers. For trading systems, reducing latency increases throughput.
- Avoiding "coordinated omission" is important for accurate latency testing.
- In Java 8, escape analysis and inlining can avoid object creation with lambdas, improving performance.
- Systems using Chronicle Queue can achieve low 25 microsecond latency while ensuring data is
Unless you have a problem which scales to many independent tasks easily e.g. web services, you may find that the best way to improve throughput is by reducing latency. This talk starts with Little's Law and it's consequences for high performance computing.
Streams and lambdas the good, the bad and the uglyPeter Lawrey
Based on a six month migration of C# code to Java 8, what is legacy lambda code likely to look like and what mistakes can be made.
Good use cases.
Bad use cases with solutions
Ugly use cases.
Responding rapidly when you have 100+ GB data sets in JavaPeter Lawrey
One way to speed up you application is to bring more of your data into memory. But how to do you handle hundreds of GB of data in a JVM and what tools can help you.
Mentions: Speedment, Azul, Terracotta, Hazelcast and Chronicle.
After migrating a three year old C# project to Java we ending up with a significant portion of legacy code using lambdas in Java. What was some of the good use cases, code which could be written better and the problems we had migrating from C#. At the end we look at the performance implications of using Lambdas.
The document discusses using MongoDB as a tick store for financial data. It provides an overview of MongoDB and its benefits for handling tick data, including its flexible data model, rich querying capabilities, native aggregation framework, ability to do pre-aggregation for continuous data snapshots, language drivers and Hadoop connector. It also presents a case study of AHL, a quantitative hedge fund, using MongoDB and Python as their market data platform to easily onboard large volumes of financial data in different formats and provide low-latency access for backtesting and research applications.
Real World MongoDB: Use Cases from Financial Services by Daniel RobertsMongoDB
This document discusses how MongoDB can help capital markets firms address challenges with traditional relational database solutions for tasks like risk analysis and reporting, market data aggregation, and reference data management. It provides examples of how MongoDB's flexible schema, replication, and sharding capabilities allow global reference data to be distributed in real-time for low-latency access. The document argues that using MongoDB can significantly reduce costs compared to existing ETL-based approaches by distributing updates immediately in a single place.
Microservices for performance - GOTO Chicago 2016Peter Lawrey
How do Microservices and Trading Systems overlap?
How can one area learn from the other?
How can we test components of microservices?
Is there a library which helps us implement and test these services?
This document discusses big data in finance. It provides an overview of the different types and sources of structured and unstructured data used in finance. It also outlines the technology landscape for working with this data, including frameworks for information retrieval, analytics, and human-computer interfaces. Real-time high-frequency trading is discussed as one application area that utilizes both structured market data feeds and GPU processing.
Introduction to OpenHFT for Melbourne Java Users GroupPeter Lawrey
Updated Introduction to Chronicle
Added Introduction to SharedHashMap, an off heap map which is persisted and shared between processes.
http://openhft.net/
The Business Case for SaaS Analytics for Salesforce.comDarren Cunningham
The document discusses the business case for on-demand analytics for Salesforce.com customers. It outlines how legacy on-premise business intelligence solutions are difficult to implement and maintain, while on-demand analytics solutions like LucidEra provide benefits such as low upfront costs, easy implementation, and the ability to analyze multiple data sources. The document provides steps for building a business case for on-demand analytics, including identifying quantifiable benefits and ROI opportunities in areas like increased revenue and reduced costs.
The business environment is changing fast. Change wrought by the pace and complexity of digital adoption and creating fundamental changes in customer behaviour, challenging the traditional environment. Bringing significant opportunities for those who embrace and adapt; but real consequences for those who don’
Partner Alliance Webinar - Sage X3 OverviewNet at Work
Sage X3 is a business management solution for mid-sized enterprises that aims to enable faster and more agile operations through an integrated and flexible platform. It provides functionality for core business processes like finance, manufacturing, supply chain, and customer management. Sage X3 promises benefits like increased productivity, faster processes, real-time insights, and the ability to adapt to changing business needs. Typical clients are companies with $25M-$1B in revenue across various industries that need an integrated solution to manage their entire business operations more efficiently.
These slides--based on the webinar hosted by leading IT analyst firm Enterprise Management Associates (EMA) and Digitate--provide insights into the impact of machine learning on managing workload automation.
The document provides recommendations for Spectra Decor, a cabinet hardware manufacturer, based on market research and a review of their operations. It recommends that Spectra join a trade organization to gain industry intelligence, invest in key performance indicators to track business progress, and establish a retail and wholesale pricing structure. It also suggests improvements to Spectra's website to make it more sales-focused and adding a customer relationship management system. Upgrading software and the operating system are also advised.
In order to acknowledge the constant efforts of these companies, Insights Success has shortlisted “Major League of IT Consulting and Staffing Solution Providers 2018”
OIES Consulting is an advisory firm that provides consulting and business development services to help enterprises accelerate their adoption of IoT and big data technologies. They also assist IoT and big data vendors with business development. Their services include workshops, audits, strategy development, ecosystem analysis, product launches, and engineering support. They work with various partner companies to offer a range of IoT, big data, analytics, and business transformation services to clients.
Sage 300 Clients: 4 Signs it’s Time to Update or Consider a New Accounting / ...Net at Work
This webinar provided an overview of Sage X3, an ERP solution. It discussed four common trigger points for businesses to consider upgrading their system: when the business has changed, the current solution is limiting growth, data is expanding rapidly, or user productivity is slowing. The presentation provided details on Sage X3's capabilities and benefits like faster processes, simpler use, and flexibility. It estimated a 177% ROI and five month payback period. A live demo showed Sage X3's interface and functionality. Attendees were invited to contact Net@Work representatives for more information.
The document discusses data analytics from the perspectives of different stakeholders and outlines some of the challenges that can arise from a lack of alignment. It notes that when vendors, technology teams, business analytics, and executive leaders do not share a unified vision of data analytics, it can lead to issues like unhealthy competition, redundancy, and hindered collaboration. The document emphasizes the importance of good management of data analytics to impact business goals and establish consistent performance metrics across teams.
Sage 100 (MAS 90) Clients: 4 Signs it’s Time to Consider a New Accounting / E...Net at Work
This webinar presentation provided an overview of Sage X3, an enterprise resource planning (ERP) software. It discussed four common trigger points that prompt businesses to upgrade their systems, including when a business has changed, the current solution is limiting growth, data is rapidly expanding, or user productivity is slowing down. The presentation demonstrated Sage X3's capabilities, including faster and simpler processes, flexibility, and expected benefits like increased productivity, overhead cost savings, and growth. Live demos showed Sage X3's role-based workspace and core applications. The conclusion discussed how Sage X3 can help businesses that are growing, changing, or evolving.
Topics including: The transformative value of real-time data and analytics, and current barriers to adoption. The importance of an end-to-end solution for data-in-motion that includes ingestion, processing, and serving. Apache Kudu’s role in simplifying real-time architectures.
Daniel Jasník - ITSMF pro cloudové služby - AID2019ALVAO
Daniel Jasník má více než 15 let zkušeností v oblasti IT, z toho 7 let v ITSM. Nyní poskytuje své konzultantské služby Enterprise zákazníkům Microsoftu v EMEA region s cílem umožnit jim dosáhnout všech výhod, které Microsoft Cloud přináší, a to aplikováním Microsoft Modern Service Management přístupu.
Sparsh provides end-to-end IT solutions to help businesses improve operations and shareholder value. With over 10 years of experience in India and abroad, they have successfully assisted over 2500 organizations. Their services include Tally implementation, web solutions, SMS solutions, payment gateways, biometric attendance, and more. Sparsh's experienced consultants can assess needs, design customized solutions, implement projects, and provide ongoing support to maximize benefits.
The document discusses a webinar on optimizing IT costs and value through usage-driven insights. It provides biographies of the two speakers, Dennis Nils Drogseth from Enterprise Management Associates and Will Degener from Scalable Software. It also outlines the agenda which will cover topics like organizational alignment, understanding usage data, how different technology areas benefit from usage insights, and how to plan for ongoing benefits.
With more consumers demanding digital solutions, how can utilities optimize their operations to meet those expectations and increase overall efficiency?
On 22 September 2016 we presented the 'art of data science' at Lord's Cricket Ground. See here a collection of the slides presented.
Many thanks to our partners: Insight, Automated Intelligence and CORETX.
See more data science here: https://redpixie.com/data-science/
How to build an it transformation roadmapInnesGerrard
An estimated 80 percent of #businesses will need to transform their current IT efforts to keep up with new business expectations and technological developments. These include investments such as cloud computing, IoT and BigData projects.
How to Prepare for 5-Minute Settlement: Everything Utilities Traders Need to ...Kaitlyn Hurley
To view as a webinar, visit: https://www.allegrodev.com/resources/5-minute-settlement-webinar/
This presentation will help utilities traders learn how to navigate volatile energy markets during the 5-Minute Settlement Era.
Discover how advanced analytics drives profit for commodity trading companies and the latest market trends for increasing business efficiency and profitability.
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.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
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.
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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/
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.