This is the story of the implementation of Activiti at Ghent University Library
Topics:
- Use Case
- Setup
- Models
- Data Driven approach
Presented at Activiti user day in Paris
Briefing room 20160920-ep017-striim - a real-time version of the truth-dez-sl...Dez Blanchfield
In less than a lifetime, data processing has accelerated from punched cards to real-time analytics, where data streams continuously in seconds, hours, or less. Early big data architectures relied on disk and RAM storage, which have plummeted in cost. Customers now expect always-on, real-time experiences across banking, recommendations, and more due to factors like social media, fitness trackers, mobile devices, and the internet of things, which generates perpetual streams of data. Current architectures have adapted to handle real-time data streams rather than batched data.
Big data: the next frontier for innovation, competition and productivityAndrea Rabbaglietti
The document discusses big data, which refers to extremely large datasets that cannot be captured, stored, managed or analyzed with traditional database tools. It notes that what qualifies as big data can vary by sector and technology. Big data today typically ranges from dozens of terabytes to multiple petabytes in size. The document outlines how big data creates value through transparency, experimentation, customization and more. It also discusses techniques like data mining and machine learning and technologies like Hadoop and Cassandra that are useful for processing and managing big data.
The document discusses the Internet of Things (IoT) and its potential. It defines IoT as connecting everyday objects and sensors to the Internet to bring operational data into business processes. IoT allows for monitoring value, just-in-time automation across many things and applications. It completes the collaboration process by leveraging data for decision making between people, processes, things, and digitization. Rapid adoption of IoT is expected, growing from around 50 billion connected objects currently to over 50 billion by 2020. Unique security and scalability challenges must be addressed to defend connected things and support millions of things per customer.
Big Data Expo 2017 - Meer grip op outsourcingscontracten met data driven cust...Marc Lelijveld
Veel bedrijven doen het al, informatie verzamelen over de tevredenheid van klanten om dit vervolgens trots te delen met potentiele klanten en concurrenten. Heijmans en Centric zijn als eerste een outsourcing overeenkomst aangegaan waarbij de ouderwetse SLA overboord is gegooid en enkel wordt gestuurd op tevredenheid. Outsourcing contractueel op basis van 90% tevreden gebruiken in plaats van 90% uptime van systemen.
Om dit inzichtelijk te maken en de juiste sturing te kunnen geven zijn enkel de resultaten van enquêtes niet voldoende. Traditionele rapportages van SCOM, SCCM, AD, PRTG, Topdesk Servicenow, etc. geven niet de benodigde inzichten en je bent al snel zoekende in een overvloed aan data verspreid over verschillende bronnen.
Door koppelen van data krijg je inzicht in de processen en kun je deze verbeteren voor het bereiken van een hogere klanttevredenheid. Sebastiaan Boonstoppel en Marc Lelijveld vertellen en laten zien hoe organisaties dit kunnen realiseren.
The document discusses democratizing big data. It begins by asking what is new about big data and why it is important. Big data involves massive amounts of data from various sources that grow rapidly. New technologies like Hadoop and NoSQL databases allow this data to be processed, stored, and analyzed efficiently and cost-effectively. This enables companies to ask new questions and get answers faster for better decision making and competitive advantage. However, adoption of big data is slow due to the difficulty of deploying and using these technologies and a lack of skilled practitioners. To democratize big data, we need tools that abstract away complexity, training and education, and a change of mindset focused on experimentation, collaboration, and being
The document discusses democratizing big data. It argues that big data adoption has been slow due to the difficulty of deploying, managing, and using big data technologies as well as a lack of skilled practitioners. To democratize big data, tools are needed to abstract away complexity, more training and education is required in areas like data science and predictive analytics, and organizations need to develop a more data-driven mindset of experimentation, collaboration, and perseverance. The keys to democratizing big data are providing better tools, education, and shifting to a new way of thinking.
This is the story of the implementation of Activiti at Ghent University Library
Topics:
- Use Case
- Setup
- Models
- Data Driven approach
Presented at Activiti user day in Paris
Briefing room 20160920-ep017-striim - a real-time version of the truth-dez-sl...Dez Blanchfield
In less than a lifetime, data processing has accelerated from punched cards to real-time analytics, where data streams continuously in seconds, hours, or less. Early big data architectures relied on disk and RAM storage, which have plummeted in cost. Customers now expect always-on, real-time experiences across banking, recommendations, and more due to factors like social media, fitness trackers, mobile devices, and the internet of things, which generates perpetual streams of data. Current architectures have adapted to handle real-time data streams rather than batched data.
Big data: the next frontier for innovation, competition and productivityAndrea Rabbaglietti
The document discusses big data, which refers to extremely large datasets that cannot be captured, stored, managed or analyzed with traditional database tools. It notes that what qualifies as big data can vary by sector and technology. Big data today typically ranges from dozens of terabytes to multiple petabytes in size. The document outlines how big data creates value through transparency, experimentation, customization and more. It also discusses techniques like data mining and machine learning and technologies like Hadoop and Cassandra that are useful for processing and managing big data.
The document discusses the Internet of Things (IoT) and its potential. It defines IoT as connecting everyday objects and sensors to the Internet to bring operational data into business processes. IoT allows for monitoring value, just-in-time automation across many things and applications. It completes the collaboration process by leveraging data for decision making between people, processes, things, and digitization. Rapid adoption of IoT is expected, growing from around 50 billion connected objects currently to over 50 billion by 2020. Unique security and scalability challenges must be addressed to defend connected things and support millions of things per customer.
Big Data Expo 2017 - Meer grip op outsourcingscontracten met data driven cust...Marc Lelijveld
Veel bedrijven doen het al, informatie verzamelen over de tevredenheid van klanten om dit vervolgens trots te delen met potentiele klanten en concurrenten. Heijmans en Centric zijn als eerste een outsourcing overeenkomst aangegaan waarbij de ouderwetse SLA overboord is gegooid en enkel wordt gestuurd op tevredenheid. Outsourcing contractueel op basis van 90% tevreden gebruiken in plaats van 90% uptime van systemen.
Om dit inzichtelijk te maken en de juiste sturing te kunnen geven zijn enkel de resultaten van enquêtes niet voldoende. Traditionele rapportages van SCOM, SCCM, AD, PRTG, Topdesk Servicenow, etc. geven niet de benodigde inzichten en je bent al snel zoekende in een overvloed aan data verspreid over verschillende bronnen.
Door koppelen van data krijg je inzicht in de processen en kun je deze verbeteren voor het bereiken van een hogere klanttevredenheid. Sebastiaan Boonstoppel en Marc Lelijveld vertellen en laten zien hoe organisaties dit kunnen realiseren.
The document discusses democratizing big data. It begins by asking what is new about big data and why it is important. Big data involves massive amounts of data from various sources that grow rapidly. New technologies like Hadoop and NoSQL databases allow this data to be processed, stored, and analyzed efficiently and cost-effectively. This enables companies to ask new questions and get answers faster for better decision making and competitive advantage. However, adoption of big data is slow due to the difficulty of deploying and using these technologies and a lack of skilled practitioners. To democratize big data, we need tools that abstract away complexity, training and education, and a change of mindset focused on experimentation, collaboration, and being
The document discusses democratizing big data. It argues that big data adoption has been slow due to the difficulty of deploying, managing, and using big data technologies as well as a lack of skilled practitioners. To democratize big data, tools are needed to abstract away complexity, more training and education is required in areas like data science and predictive analytics, and organizations need to develop a more data-driven mindset of experimentation, collaboration, and perseverance. The keys to democratizing big data are providing better tools, education, and shifting to a new way of thinking.
Covid is accelerating the need for digital transformation, but organisational and data silos stand in the way. With transformation failure rates of 84% (McKinsey) how do we make sure your business doesn't sink?
The answer is to use Business Architecture to remove organisational and structural silos, and to use Data Architecture to remove data silos so that agility and scalability can be built ground-up.
Big data refers to large volumes of diverse data that organizations collect from various sources. It is characterized by its volume, velocity, and variety. While the amount of data is large, it is how organizations use the data that provides value. Many sectors have adopted big data including banking, education, healthcare, and companies seeking to improve search quality. Big data emerged in the 2000s and can help reduce costs, improve products and services, and speed up processes.
The big data revolution in healthcare by joel selanikioDarpan Deoghare
The document discusses the transition in healthcare from collecting paper medical records to using big data. It notes that paper records became obsolete quickly and collecting data by visiting thousands of homes took months or years. The development of technology like PalmPilots and cloud-based apps made data collection and analysis more convenient and faster. The author created a cloud-based software called MAGPI that reduced a previously two-year long data collection process to just five minutes, demonstrating how technology improved efficiency. Finally, the document advocates that healthcare and businesses should make use of real-time big data and technology to better organize and analyze data from millions of people to facilitate timely decision-making.
The first phase of big data (Big Data 1.0) was all about “getting it.” The more data we had, the better the targeting, measurement and insights capabilities we could attain.
The big data ecosystem has now reached a tipping point where the basic infrastructural capabilities for supporting big data challenges and opportunities are easily available. Now we are entering what we would call the next generation of big data — big data 2.0
Big data analytics involves capturing, storing, processing, analyzing, and visualizing huge quantities of information from a variety of sources. This data is characterized by its volume, variety, velocity, veracity, variability, and complexity. Traditional analytics are not suited to handle big data due to its size and constantly changing nature. By analyzing patterns in big data, businesses can gain insights to improve processes and campaigns. However, specialized software is needed to make sense of big data's different types and formats from numerous sources. The right big data solution depends on an organization's specific data, budgets, skills, and future needs.
SOA enables a more pragmatic approach to planning IT strategies and changes. It provides a service architecture framework aligned with business goals and processes. This framework can be used to 1) identify scenarios to close gaps between current IT and business needs, 2) make tactical decisions on scenarios to implement, and 3) continuously optimize by returning to steps 1 and 2. The service architecture preserves viable existing assets, replaces outdated ones, and adds new capabilities, similar to how city planners manage infrastructure changes.
The document discusses how the amount of data in the world is growing exponentially and will quadruple this year. While companies have access to vast amounts of data, they are struggling to make sense of it and extract useful insights. The key is providing relevant context to data in order to understand how different data points relate and using visual analytics to weave intelligence directly into core business processes. This allows people to constantly adapt to changing environments.
May 2013 Federal Cloud Computing Summit Keynote by David CearlyTim Harvey
This presentation discusses key issues related to cloud computing adoption and trends. It provides an overview of the current state of cloud computing including survey results on adoption levels and preferred cloud approaches across different industries. Key topics covered include the growth of SaaS, PaaS, and IaaS spending, expectations for hybrid cloud strategies, and a strategic model for how agencies should approach cloud computing.
This is a presentation for the HBC analysis on the article "THE NEW PATTERNS OF INOVATION" during the internship under the guidance of Prof.Sameer Mathur {Ph.D,Carnige Mellon} ,IIM-LUCKNOW
#ESGJRConsultingInc #Software #Cisco #Network #Engineering #CNSVitalSigns #DNAIDSmartCard
Cisco Certifications
Go to www.esgjrconsultinginc.com to learn more about Software/Network Engineering Projects.
Simon Thomas - Big Data: New Opportunity, New RiskHoi Lan Leong
This document discusses big data and its growing importance and risks for businesses. It notes that big data is characterized by its volume, variety, velocity, and veracity. The amount of data being generated is growing exponentially from many sources, both within and outside of companies' control. While big data currently provides improved insights, it is becoming increasingly critical for business functions and performance. As businesses rely more on external sources of big data, they need strategies to manage the new risks and ensure stability of these critical data channels despite being outside their control.
This document discusses cloud and open source GIS. It highlights benefits like automated change management, flexible digital delivery, and data accuracy improvement. Open source is important because it allows for group collaboration, crowd sourcing, and benefits from many eyeballs finding bugs. Open source is now widely used including by 90% of supercomputers, 60% of internet servers, and 30% of smartphones. The cloud provides scalability and hosting for GIS applications and has enabled enterprise mapping, open data standards, and greater spatial analysis and adoption of GIS. While government has been slow to change, cloud and open source can help make government more transparent, efficient and user-oriented. There are still issues to address regarding data protection, security, standards
HESCA23 - Joe Keating - Data Analytics for Engagement Presentation.pptxJoe Keating
This document discusses how data analytics can help optimize student engagement and retention. It defines student engagement as the attention, curiosity, interest, optimism, and passion students show when learning. Data analytics allows institutions to capture a clear picture of engagement factors like attendance, interaction, and feedback, identify early warning signs of disengagement, and empower proactive interventions. Studies show institutions using data analytics for personalization have higher student engagement, satisfaction, and retention rates compared to those not using these tools.
Techconnect live joe keating - what can data do for you - 16x9Joe Keating
This document discusses how data can provide value to businesses by providing real-life examples. It explains that not all data has value and businesses should look to their objectives to identify valuable data. Two examples are given of how data can help reduce customer churn and increase revenue. Common barriers to realizing data value, such as tool-centric and IT-centric approaches, are outlined. The document recommends aligning data initiatives with business objectives, empowering business users to access data directly, and embracing technology through a flexible data platform to overcome these barriers.
Techconnect Live 2019 - Joe Keating - what can data do for you?Joe Keating
This document discusses how data can provide value to businesses. It provides two examples of how data can be used to reduce customer churn and increase revenue. The barriers to realizing this value are identified as tool-centric and IT-centric approaches that do not align data initiatives to business objectives. The document recommends empowering business users to access data themselves and embracing technology like a data platform to remove these barriers.
Smart retail & hospitality 2019 joe keating - retail automationJoe Keating
This presentation covers the topic of Robotic Process Automation, including an overview of why, where and how it works. There are some Retail based use cases also explored.
Covid is accelerating the need for digital transformation, but organisational and data silos stand in the way. With transformation failure rates of 84% (McKinsey) how do we make sure your business doesn't sink?
The answer is to use Business Architecture to remove organisational and structural silos, and to use Data Architecture to remove data silos so that agility and scalability can be built ground-up.
Big data refers to large volumes of diverse data that organizations collect from various sources. It is characterized by its volume, velocity, and variety. While the amount of data is large, it is how organizations use the data that provides value. Many sectors have adopted big data including banking, education, healthcare, and companies seeking to improve search quality. Big data emerged in the 2000s and can help reduce costs, improve products and services, and speed up processes.
The big data revolution in healthcare by joel selanikioDarpan Deoghare
The document discusses the transition in healthcare from collecting paper medical records to using big data. It notes that paper records became obsolete quickly and collecting data by visiting thousands of homes took months or years. The development of technology like PalmPilots and cloud-based apps made data collection and analysis more convenient and faster. The author created a cloud-based software called MAGPI that reduced a previously two-year long data collection process to just five minutes, demonstrating how technology improved efficiency. Finally, the document advocates that healthcare and businesses should make use of real-time big data and technology to better organize and analyze data from millions of people to facilitate timely decision-making.
The first phase of big data (Big Data 1.0) was all about “getting it.” The more data we had, the better the targeting, measurement and insights capabilities we could attain.
The big data ecosystem has now reached a tipping point where the basic infrastructural capabilities for supporting big data challenges and opportunities are easily available. Now we are entering what we would call the next generation of big data — big data 2.0
Big data analytics involves capturing, storing, processing, analyzing, and visualizing huge quantities of information from a variety of sources. This data is characterized by its volume, variety, velocity, veracity, variability, and complexity. Traditional analytics are not suited to handle big data due to its size and constantly changing nature. By analyzing patterns in big data, businesses can gain insights to improve processes and campaigns. However, specialized software is needed to make sense of big data's different types and formats from numerous sources. The right big data solution depends on an organization's specific data, budgets, skills, and future needs.
SOA enables a more pragmatic approach to planning IT strategies and changes. It provides a service architecture framework aligned with business goals and processes. This framework can be used to 1) identify scenarios to close gaps between current IT and business needs, 2) make tactical decisions on scenarios to implement, and 3) continuously optimize by returning to steps 1 and 2. The service architecture preserves viable existing assets, replaces outdated ones, and adds new capabilities, similar to how city planners manage infrastructure changes.
The document discusses how the amount of data in the world is growing exponentially and will quadruple this year. While companies have access to vast amounts of data, they are struggling to make sense of it and extract useful insights. The key is providing relevant context to data in order to understand how different data points relate and using visual analytics to weave intelligence directly into core business processes. This allows people to constantly adapt to changing environments.
May 2013 Federal Cloud Computing Summit Keynote by David CearlyTim Harvey
This presentation discusses key issues related to cloud computing adoption and trends. It provides an overview of the current state of cloud computing including survey results on adoption levels and preferred cloud approaches across different industries. Key topics covered include the growth of SaaS, PaaS, and IaaS spending, expectations for hybrid cloud strategies, and a strategic model for how agencies should approach cloud computing.
This is a presentation for the HBC analysis on the article "THE NEW PATTERNS OF INOVATION" during the internship under the guidance of Prof.Sameer Mathur {Ph.D,Carnige Mellon} ,IIM-LUCKNOW
#ESGJRConsultingInc #Software #Cisco #Network #Engineering #CNSVitalSigns #DNAIDSmartCard
Cisco Certifications
Go to www.esgjrconsultinginc.com to learn more about Software/Network Engineering Projects.
Simon Thomas - Big Data: New Opportunity, New RiskHoi Lan Leong
This document discusses big data and its growing importance and risks for businesses. It notes that big data is characterized by its volume, variety, velocity, and veracity. The amount of data being generated is growing exponentially from many sources, both within and outside of companies' control. While big data currently provides improved insights, it is becoming increasingly critical for business functions and performance. As businesses rely more on external sources of big data, they need strategies to manage the new risks and ensure stability of these critical data channels despite being outside their control.
This document discusses cloud and open source GIS. It highlights benefits like automated change management, flexible digital delivery, and data accuracy improvement. Open source is important because it allows for group collaboration, crowd sourcing, and benefits from many eyeballs finding bugs. Open source is now widely used including by 90% of supercomputers, 60% of internet servers, and 30% of smartphones. The cloud provides scalability and hosting for GIS applications and has enabled enterprise mapping, open data standards, and greater spatial analysis and adoption of GIS. While government has been slow to change, cloud and open source can help make government more transparent, efficient and user-oriented. There are still issues to address regarding data protection, security, standards
HESCA23 - Joe Keating - Data Analytics for Engagement Presentation.pptxJoe Keating
This document discusses how data analytics can help optimize student engagement and retention. It defines student engagement as the attention, curiosity, interest, optimism, and passion students show when learning. Data analytics allows institutions to capture a clear picture of engagement factors like attendance, interaction, and feedback, identify early warning signs of disengagement, and empower proactive interventions. Studies show institutions using data analytics for personalization have higher student engagement, satisfaction, and retention rates compared to those not using these tools.
Techconnect live joe keating - what can data do for you - 16x9Joe Keating
This document discusses how data can provide value to businesses by providing real-life examples. It explains that not all data has value and businesses should look to their objectives to identify valuable data. Two examples are given of how data can help reduce customer churn and increase revenue. Common barriers to realizing data value, such as tool-centric and IT-centric approaches, are outlined. The document recommends aligning data initiatives with business objectives, empowering business users to access data directly, and embracing technology through a flexible data platform to overcome these barriers.
Techconnect Live 2019 - Joe Keating - what can data do for you?Joe Keating
This document discusses how data can provide value to businesses. It provides two examples of how data can be used to reduce customer churn and increase revenue. The barriers to realizing this value are identified as tool-centric and IT-centric approaches that do not align data initiatives to business objectives. The document recommends empowering business users to access data themselves and embracing technology like a data platform to remove these barriers.
Smart retail & hospitality 2019 joe keating - retail automationJoe Keating
This presentation covers the topic of Robotic Process Automation, including an overview of why, where and how it works. There are some Retail based use cases also explored.
This presentation was the basis for my talk at Tech Connect Live 2018 (IT & Data Summit) at the RDS in Dublin on 30th May 2018. The slides are based on applying a simple approach to the management and optimization of Data within Organisations today.
This presentation provides a summary of how MDS Custom Extensions can enhance the native functionality of MDS and also highlights the steps involved in creating an extension.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
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.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
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
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
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/
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
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.
3. Live
Updates
from
Everywhere
Easy Access
to High
Quality Data
from
Anywhere
Effective
Intelligence
as Things
Happen
Simple
Digital Data
Capture
Clear Data
Ownership
One Master
Data
Repository
for Everyone
Solving The Big Issues