This document discusses Paradigm4 Inc., a company that provides a database called SciDB for complex analytics on large datasets. SciDB uses an array data model that is more efficient for storing and analyzing sensor, geospatial, temporal and machine-generated data compared to relational databases. It allows for distributed, scalable storage and parallel processing of data and mathematical operations. SciDB is open source and can be run on commodity hardware or cloud infrastructure. It is well-suited for applications in various industries involving large and complex datasets.
Complex analytics should work as nimbly on extremely large data sets as on small ones. You don’t want to think about whether your data fits in-memory, about parallelism, or formatting data for math packages. You’d like to use your favorite analytical language and have it transparently scale up to Big Data volumes.
Paradigm4 presents a webinar about SciDB—the massively scalable, open source, array database with native complex analytics, integrated with R and Python.
Details:
Presenter: Bryan Lewis, Chief Data Scientist, Paradigm4
Day/Time: Tuesday November 12th, 2013 at 1pm EST
Learn how SciDB enables you to:
-Explore rich data sets interactively
-Do complex math in-database—without being constrained -by memory limitations
-Perform multi-dimensional windowing, filtering, and aggregation
-Offload large computations to a commodity hardware cluster—on-premise or in a cloud
-Use R and Python to analyze SciDB arrays as if they were R or Python objects.
-Share data among users, with multi-user data integrity guarantees and version control
Webinar Agenda:
-Introduction to SciDB
-Demo
-Live Q&A
Massively Scalable Computational Finance with SciDBParadigm4Inc
Hedge funds, investment managers and prop shops need to keep pace with rapidly growing data volumes from many sources.
SciDB—an advanced computational database programmable from R and Python—scales out to petabyte volumes and facilitates rapid integration of diverse data sources. Open source and running on commodity hardware, SciDB is extensible and scales cost effectively.
Attend this webinar to learn how quants and system developers harness SciDB’s massively scalable complex analytics to solve hard problems faster. SciDB’s native array storage is optimized for time-series data, delivering fast windowed aggregates and complex analytics, without time-consuming data extraction.
Webinar presenters will demonstrate real world use cases, including the ability to quickly:
1. Generate aggregated order books across multiple exchanges
2. Create adjusted continuous futures contracts
3. Analyze complex financial networks to detect anomalous behavior
Using Cloud Automation Technologies to Deliver an Enterprise Data FabricCambridge Semantics
The world of database management is changing. Cloud adoption is accelerating, offering a path for companies to increase their database capabilities while keeping costs in line. To help IT decision-makers survive and thrive in the cloud era, DBTA hosted this special roundtable webinar.
Debunking "Purpose-Built Data Systems:": Enter the Universal DatabaseStavros Papadopoulos
Purpose-built databases and platforms have actually created more complexity, effort, and unnecessary reinvention. The status quo is a big mess. TileDB took the opposite approach.
In this presentation, Stavros, the original creator of TileDB, shared the underlying principles of the TileDB universal database built on multi-dimensional arrays, making the case for it as a true first in the data management industry.
The Business Case for Semantic Web Ontology & Knowledge GraphCambridge Semantics
In this webinar Mark Wallace, Ontologist & Developer, Semantic Arts, and Thomas Cook, Director of Sales AnzoGraph DB, Cambridge Semantics, explore the benefits of building a Semantic Knowledge Graph with RDF*, wrapping up with an airline data demo that illustrates the value of schema, inference and reasoning in it.
Should a Graph Database Be in Your Next Data Warehouse Stack?Cambridge Semantics
In this webinar, AnzoGraph’s graph database guru Barry Zane (former co-founder of Netezza) and data governance author Steve Sarsfield talk about how graph databases fit into the data warehouse modernization trend. They also explore how certain workloads can be better served with an analytical graph database and how today’s technology stacks offer new paradigms for deployment like the cloud, containers and graph analytics.
Counting Unique Users in Real-Time: Here's a Challenge for You!DataWorks Summit
Finding the number of unique users out of 10 billion events per day is challenging. At this session, we're going to describe how re-architecting our data infrastructure, relying on Druid and ThetaSketch, enables our customers to obtain these insights in real-time.
To put things into context, at NMC (Nielsen Marketing Cloud) we provide our customers (marketers and publishers) real-time analytics tools to profile their target audiences. Specifically, we provide them with the ability to see the number of unique users who meet a given criterion.
Historically, we have used Elasticsearch to answer these types of questions, however, we have encountered major scaling and stability issues.
In this presentation we will detail the journey of rebuilding our data infrastructure, including researching, benchmarking and productionizing a new technology, Druid, with ThetaSketch, to overcome the limitations we were facing.
We will also provide guidelines and best practices with regards to Druid.
Topics include :
* The need and possible solutions
* Intro to Druid and ThetaSketch
* How we use Druid
* Guidelines and pitfalls
Complex analytics should work as nimbly on extremely large data sets as on small ones. You don’t want to think about whether your data fits in-memory, about parallelism, or formatting data for math packages. You’d like to use your favorite analytical language and have it transparently scale up to Big Data volumes.
Paradigm4 presents a webinar about SciDB—the massively scalable, open source, array database with native complex analytics, integrated with R and Python.
Details:
Presenter: Bryan Lewis, Chief Data Scientist, Paradigm4
Day/Time: Tuesday November 12th, 2013 at 1pm EST
Learn how SciDB enables you to:
-Explore rich data sets interactively
-Do complex math in-database—without being constrained -by memory limitations
-Perform multi-dimensional windowing, filtering, and aggregation
-Offload large computations to a commodity hardware cluster—on-premise or in a cloud
-Use R and Python to analyze SciDB arrays as if they were R or Python objects.
-Share data among users, with multi-user data integrity guarantees and version control
Webinar Agenda:
-Introduction to SciDB
-Demo
-Live Q&A
Massively Scalable Computational Finance with SciDBParadigm4Inc
Hedge funds, investment managers and prop shops need to keep pace with rapidly growing data volumes from many sources.
SciDB—an advanced computational database programmable from R and Python—scales out to petabyte volumes and facilitates rapid integration of diverse data sources. Open source and running on commodity hardware, SciDB is extensible and scales cost effectively.
Attend this webinar to learn how quants and system developers harness SciDB’s massively scalable complex analytics to solve hard problems faster. SciDB’s native array storage is optimized for time-series data, delivering fast windowed aggregates and complex analytics, without time-consuming data extraction.
Webinar presenters will demonstrate real world use cases, including the ability to quickly:
1. Generate aggregated order books across multiple exchanges
2. Create adjusted continuous futures contracts
3. Analyze complex financial networks to detect anomalous behavior
Using Cloud Automation Technologies to Deliver an Enterprise Data FabricCambridge Semantics
The world of database management is changing. Cloud adoption is accelerating, offering a path for companies to increase their database capabilities while keeping costs in line. To help IT decision-makers survive and thrive in the cloud era, DBTA hosted this special roundtable webinar.
Debunking "Purpose-Built Data Systems:": Enter the Universal DatabaseStavros Papadopoulos
Purpose-built databases and platforms have actually created more complexity, effort, and unnecessary reinvention. The status quo is a big mess. TileDB took the opposite approach.
In this presentation, Stavros, the original creator of TileDB, shared the underlying principles of the TileDB universal database built on multi-dimensional arrays, making the case for it as a true first in the data management industry.
The Business Case for Semantic Web Ontology & Knowledge GraphCambridge Semantics
In this webinar Mark Wallace, Ontologist & Developer, Semantic Arts, and Thomas Cook, Director of Sales AnzoGraph DB, Cambridge Semantics, explore the benefits of building a Semantic Knowledge Graph with RDF*, wrapping up with an airline data demo that illustrates the value of schema, inference and reasoning in it.
Should a Graph Database Be in Your Next Data Warehouse Stack?Cambridge Semantics
In this webinar, AnzoGraph’s graph database guru Barry Zane (former co-founder of Netezza) and data governance author Steve Sarsfield talk about how graph databases fit into the data warehouse modernization trend. They also explore how certain workloads can be better served with an analytical graph database and how today’s technology stacks offer new paradigms for deployment like the cloud, containers and graph analytics.
Counting Unique Users in Real-Time: Here's a Challenge for You!DataWorks Summit
Finding the number of unique users out of 10 billion events per day is challenging. At this session, we're going to describe how re-architecting our data infrastructure, relying on Druid and ThetaSketch, enables our customers to obtain these insights in real-time.
To put things into context, at NMC (Nielsen Marketing Cloud) we provide our customers (marketers and publishers) real-time analytics tools to profile their target audiences. Specifically, we provide them with the ability to see the number of unique users who meet a given criterion.
Historically, we have used Elasticsearch to answer these types of questions, however, we have encountered major scaling and stability issues.
In this presentation we will detail the journey of rebuilding our data infrastructure, including researching, benchmarking and productionizing a new technology, Druid, with ThetaSketch, to overcome the limitations we were facing.
We will also provide guidelines and best practices with regards to Druid.
Topics include :
* The need and possible solutions
* Intro to Druid and ThetaSketch
* How we use Druid
* Guidelines and pitfalls
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricCambridge Semantics
Watch this webinar to learn about the benefits of using semantic and graph database technology to create a Data Catalog of all of an enterprise's data, regardless of source or format, as part of a modern IT or data management stack and an important step toward building an Enterprise Data Fabric.
Slides from the August 2021 St. Louis Big Data IDEA meeting from Sam Portillo. The presentation covers AWS EMR including comparisons to other similar projects and lessons learned. A recording is available in the comments for the meeting.
Promote the Good of the People of the United Kingdom by Maintaining Monetary ...DataWorks Summit
The Bank of England is the central Bank of the United Kingdom, established in 1694. Representatives from the Bank’s Data Analytics & Modelling team will discuss the Bank of England's journey to delivering a Big Data capability and how the Hortonworks HDP platform is helping us deliver on our mission statement of “promote the good of the people of the United Kingdom by maintaining monetary and financial stability". We will explore the challenges we've faced, how we have overcome some of these and those that remain to be conquered. We will also present our strategy for the Bank’s future Big Data platform as we look to scale up further in the coming years.
We will focus in particular on our first successful ‘Big Data’ production system. This exists in response to the financial crises of 2008 and the subsequent push to make the derivative markets safer by reducing systemic risk. In Europe this was delivered through the European Market Infrastructure Regulation (EMIR). We will explain the Bank of England’s role in monitoring UK entities within this important market and describe the significant challenges facing our team in building a data analytics platform to facilitate this
Speakers
Nick Vaughan, Domain SME - Data Analytics & Modelling
Bank of England
Adrian Waddy, Technical Lead
Bank of England
Big Data Streams Architectures. Why? What? How?Anton Nazaruk
With a current zoo of technologies and different ways of their interaction it's a big challenge to architect a system (or adopt existed one) that will conform to low-latency BigData analysis requirements. Apache Kafka and Kappa Architecture in particular take more and more attention over classic Hadoop-centric technologies stack. New Consumer API put significant boost in this direction. Microservices-based streaming processing and new Kafka Streams tend to be a synergy in BigData world.
Risk Analytics Using Knowledge Graphs / FIBO with Deep LearningCambridge Semantics
This EDM Council webinar, sponsored by Cambridge Semantics Inc. and featuring FI Consulting, explores the challenges common to a risk analytics pipeline, application of graph analytics to mortgage loan data and use cases in adjacent areas including customer service, collections, fraud and AML.
In this webinar, data analytics gurus Sathish Thyagarajan and Steve Sarsfield introduce AnzoGraph™, our graph OLAP database, demonstrate the different types of analyses you can perform with it and how it complements Neo4j, AWS Neptune and other OLTP systems. Finally, they’ll show how you can get it up and running on your laptop in about 5 minutes.
Data and analytics are at the heart of the digital transformation. Implementing a modern data platform can be challenging; moreover, success requires a shift in culture. Andreas will discuss the ways Munich Re drives cultural and technological change within their company, focusing on three key elements: people, processes, and technology. What does it mean to be a data-driven organization? How can we provide self-service analytics to our internal and external customers in an agile way? How do we get the most value out of our big data lake? How does Munich Re balance technology and culture to meet the data demands of their business?
Speaker
Andreas Kohlmaier, Head of Data Engineering, Munich Re
Here I talk about examples and use cases for Big Data & Big Data Analytics and how we accomplished massive-scale sentiment, campaign and marketing analytics for Razorfish using a collecting of database, Big Data and analytics technologies.
Recently, in the fields Business Intelligence and Data Management, everybody is talking about data science, machine learning, predictive analytics and many other “clever” terms with promises to turn your data into gold. In this slides, we present the big picture of data science and machine learning. First, we define the context for data mining from BI perspective, and try to clarify various buzzwords in this field. Then we give an overview of the machine learning paradigms. After that, we are going to discuss - at a high level - the various data mining tasks, techniques and applications. Next, we will have a quick tour through the Knowledge Discovery Process. Screenshots from demos will be shown, and finally we conclude with some takeaway points.
Applying Noisy Knowledge Graphs to Real ProblemsDataWorks Summit
Knowledge graphs (KGs) have recently emerged as a powerful way to represent knowledge in multiple communities, including data mining, natural language processing and machine learning. Large-scale KGs like Wikidata and DBpedia are openly available, while in industry, the Google Knowledge Graph is a good example of proprietary knowledge that continues to fuel impressive advances in Google's semantic search capabilities. Yet, both crowdsourced and automatically constructed KGs suffer from noise, both during KG construction and during search and inference. In this talk, I will discuss how to build and use such knowledge graphs effectively, despite the noise and sparsity of labeled data, to solve real-world social problems such as providing insights in disaster situations, and helping law enforcement fight human trafficking. I will conclude by providing insight on the lessons learned, and the applicability of research techniques to industrial problems. The talk will be designed to appeal both to business and technical leaders.
Sustainability Investment Research Using Cognitive AnalyticsCambridge Semantics
In this webinar Anthony J. Sarkis, Chief Strategy Officer at Parabole, and Steve Sarsfield, VP Product at Cambridge Semantics, explore how portfolio managers are using the recently developed Parabole/ AnzoGraph DB integration as their underlying infrastructure for conducting ML and cognitive analytics at scale to exploit data to identify potential risks and new opportunities.
Fireside Chat with Bloor Research: State of the Graph Database Market 2020Cambridge Semantics
Sean Martin, CTO of Cambridge Semantics, Philip Howard, Research Director at Bloor Research and co-author of “Graph Database Market Update 2020”, and Steve Sarsfield, VP of Product at Cambridge Semantics, hold a fireside chat on the State of the Graph Database Market.
In their webinar "Big Data Fabric 2.0 Drives Data Democratization" Ben Szekley, Cambridge Semantics’ SVP of Field Operations, and guest speaker, Forrester’s Noel Yuhanna, author of the Forrester report: “Big Data Fabric 2.0 Drives Data Democratization”, explored why data-driven businesses are making a big data fabric part of their data strategy to minimize data complexity, integrate siloed data, deliver real-time trusted insights, and to create new business opportunities. These are the slides from that webinar.
LendingClub RealTime BigData Platform with Oracle GoldenGateRajit Saha
LendingClub RealTime BigData Platform with Oracle GoldenGate BigData Adapter. This was presented at Oracle Open World 2017 at San Francisco.
Speaker :
Rajit Saha
Vengata Guruswami
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricCambridge Semantics
Watch this webinar to learn about the benefits of using semantic and graph database technology to create a Data Catalog of all of an enterprise's data, regardless of source or format, as part of a modern IT or data management stack and an important step toward building an Enterprise Data Fabric.
Slides from the August 2021 St. Louis Big Data IDEA meeting from Sam Portillo. The presentation covers AWS EMR including comparisons to other similar projects and lessons learned. A recording is available in the comments for the meeting.
Promote the Good of the People of the United Kingdom by Maintaining Monetary ...DataWorks Summit
The Bank of England is the central Bank of the United Kingdom, established in 1694. Representatives from the Bank’s Data Analytics & Modelling team will discuss the Bank of England's journey to delivering a Big Data capability and how the Hortonworks HDP platform is helping us deliver on our mission statement of “promote the good of the people of the United Kingdom by maintaining monetary and financial stability". We will explore the challenges we've faced, how we have overcome some of these and those that remain to be conquered. We will also present our strategy for the Bank’s future Big Data platform as we look to scale up further in the coming years.
We will focus in particular on our first successful ‘Big Data’ production system. This exists in response to the financial crises of 2008 and the subsequent push to make the derivative markets safer by reducing systemic risk. In Europe this was delivered through the European Market Infrastructure Regulation (EMIR). We will explain the Bank of England’s role in monitoring UK entities within this important market and describe the significant challenges facing our team in building a data analytics platform to facilitate this
Speakers
Nick Vaughan, Domain SME - Data Analytics & Modelling
Bank of England
Adrian Waddy, Technical Lead
Bank of England
Big Data Streams Architectures. Why? What? How?Anton Nazaruk
With a current zoo of technologies and different ways of their interaction it's a big challenge to architect a system (or adopt existed one) that will conform to low-latency BigData analysis requirements. Apache Kafka and Kappa Architecture in particular take more and more attention over classic Hadoop-centric technologies stack. New Consumer API put significant boost in this direction. Microservices-based streaming processing and new Kafka Streams tend to be a synergy in BigData world.
Risk Analytics Using Knowledge Graphs / FIBO with Deep LearningCambridge Semantics
This EDM Council webinar, sponsored by Cambridge Semantics Inc. and featuring FI Consulting, explores the challenges common to a risk analytics pipeline, application of graph analytics to mortgage loan data and use cases in adjacent areas including customer service, collections, fraud and AML.
In this webinar, data analytics gurus Sathish Thyagarajan and Steve Sarsfield introduce AnzoGraph™, our graph OLAP database, demonstrate the different types of analyses you can perform with it and how it complements Neo4j, AWS Neptune and other OLTP systems. Finally, they’ll show how you can get it up and running on your laptop in about 5 minutes.
Data and analytics are at the heart of the digital transformation. Implementing a modern data platform can be challenging; moreover, success requires a shift in culture. Andreas will discuss the ways Munich Re drives cultural and technological change within their company, focusing on three key elements: people, processes, and technology. What does it mean to be a data-driven organization? How can we provide self-service analytics to our internal and external customers in an agile way? How do we get the most value out of our big data lake? How does Munich Re balance technology and culture to meet the data demands of their business?
Speaker
Andreas Kohlmaier, Head of Data Engineering, Munich Re
Here I talk about examples and use cases for Big Data & Big Data Analytics and how we accomplished massive-scale sentiment, campaign and marketing analytics for Razorfish using a collecting of database, Big Data and analytics technologies.
Recently, in the fields Business Intelligence and Data Management, everybody is talking about data science, machine learning, predictive analytics and many other “clever” terms with promises to turn your data into gold. In this slides, we present the big picture of data science and machine learning. First, we define the context for data mining from BI perspective, and try to clarify various buzzwords in this field. Then we give an overview of the machine learning paradigms. After that, we are going to discuss - at a high level - the various data mining tasks, techniques and applications. Next, we will have a quick tour through the Knowledge Discovery Process. Screenshots from demos will be shown, and finally we conclude with some takeaway points.
Applying Noisy Knowledge Graphs to Real ProblemsDataWorks Summit
Knowledge graphs (KGs) have recently emerged as a powerful way to represent knowledge in multiple communities, including data mining, natural language processing and machine learning. Large-scale KGs like Wikidata and DBpedia are openly available, while in industry, the Google Knowledge Graph is a good example of proprietary knowledge that continues to fuel impressive advances in Google's semantic search capabilities. Yet, both crowdsourced and automatically constructed KGs suffer from noise, both during KG construction and during search and inference. In this talk, I will discuss how to build and use such knowledge graphs effectively, despite the noise and sparsity of labeled data, to solve real-world social problems such as providing insights in disaster situations, and helping law enforcement fight human trafficking. I will conclude by providing insight on the lessons learned, and the applicability of research techniques to industrial problems. The talk will be designed to appeal both to business and technical leaders.
Sustainability Investment Research Using Cognitive AnalyticsCambridge Semantics
In this webinar Anthony J. Sarkis, Chief Strategy Officer at Parabole, and Steve Sarsfield, VP Product at Cambridge Semantics, explore how portfolio managers are using the recently developed Parabole/ AnzoGraph DB integration as their underlying infrastructure for conducting ML and cognitive analytics at scale to exploit data to identify potential risks and new opportunities.
Fireside Chat with Bloor Research: State of the Graph Database Market 2020Cambridge Semantics
Sean Martin, CTO of Cambridge Semantics, Philip Howard, Research Director at Bloor Research and co-author of “Graph Database Market Update 2020”, and Steve Sarsfield, VP of Product at Cambridge Semantics, hold a fireside chat on the State of the Graph Database Market.
In their webinar "Big Data Fabric 2.0 Drives Data Democratization" Ben Szekley, Cambridge Semantics’ SVP of Field Operations, and guest speaker, Forrester’s Noel Yuhanna, author of the Forrester report: “Big Data Fabric 2.0 Drives Data Democratization”, explored why data-driven businesses are making a big data fabric part of their data strategy to minimize data complexity, integrate siloed data, deliver real-time trusted insights, and to create new business opportunities. These are the slides from that webinar.
LendingClub RealTime BigData Platform with Oracle GoldenGateRajit Saha
LendingClub RealTime BigData Platform with Oracle GoldenGate BigData Adapter. This was presented at Oracle Open World 2017 at San Francisco.
Speaker :
Rajit Saha
Vengata Guruswami
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Deploying Enterprise Scale Deep Learning in Actuarial Modeling at NationwideDatabricks
The traditional approach to insurance pricing involves fitting a generalized linear model (GLM) to data collected on historical claims payments and premiums received. The explosive growth in data availability and increasing competitiveness in the marketplace are challenging actuaries to find new insights in their data and make predictions with more granularity, improved speed and efficiency, and with tighter integration among business units to support strategic decisions.
In this session we will share our experience implementing deep hierarchical neural networks using TensorFlow and PySpark on Databricks. We will discuss the benefits of the ML Runtime, our experience using the goofys mount, our process for hyperparameter tuning, specific considerations for the large dataset size and extreme volatility present in insurance data, among other topics.
Authors: Bryn Clark, Krish Rajaram
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...Denodo
Watch full webinar here: https://bit.ly/35FUn32
Presented at CDAO New Zealand
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python, and Scala put advanced techniques at the fingertips of the data scientists.
However, most architecture laid out to enable data scientists miss two key challenges:
- Data scientists spend most of their time looking for the right data and massaging it into a usable format
- Results and algorithms created by data scientists often stay out of the reach of regular data analysts and business users
Watch this session on-demand to understand how data virtualization offers an alternative to address these issues and can accelerate data acquisition and massaging. And a customer story on the use of Machine Learning with data virtualization.
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
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Introducing Trillium DQ for Big Data: Powerful Profiling and Data Quality for...Precisely
The advanced analytics and AI that run today’s businesses rely on a larger volume, and greater variety, of data. This data needs to be of the highest quality to ensure the best possible outcomes, but traditional data quality tools weren’t designed for today’s modern data environments.
That’s why we’ve developed Trillium DQ for Big Data -- an integrated product that delivers industry-leading data profiling and data quality at scale, in the cloud or on premises.
In this on-demand webcast, you will learn how Trillium DQ:
• Empowers data analysts to easily profile large, diverse data sources to discover new insights, uncover issues, and report on their findings – all without involving IT.
• Delivers best-in-class entity resolution to support mission-critical applications such as Customer 360, fraud detection, AML, and predictive analytics.
• Supports Cloud and hybrid architectures by providing consistent high-performance processing within critical time windows on all platforms.
• Keeps enterprise data lakes validated, clean, and trusted with the highest quality data – without technical expertise in big data or distributed architectures.
• Enables data quality monitoring based on targeted business rules for data governance and business insight
The New Trillium DQ: Big Data Insights When and Where You Need ThemPrecisely
Organizations are increasingly challenged to deliver on new initiatives with more data sources and higher volumes of data across divergent, hybrid architectures. With this enterprise challenge in mind, Syncsort introduces Trillium DQ version 16 bringing the full range of data quality functionality forward into a highly scalable, natively executed framework that works on both traditional and distributed platforms to ensure consistency of processing while achieving the performance necessary for today’s workloads and data volumes.
This webcast highlights the capabilities of Trillium DQ v16 with a focus on its highly scalable, distributed architecture.
View this webinar on-demand to learn:
• How Trillium Discovery provides easy-to-use insight into Big Data, relational, and text-based data sources for rapid understanding of your data sources
• How Trillium Quality delivers high-scale, high-performance execution for critical data quality processes including global data enrichment and multi-domain entity resolution
Predictive Analytics - Big Data Warehousing Meetup, ZementisCaserta
Predictive analytics has always been about the future, and the age of big data has made that future an increasingly dynamic place, filled with opportunity and risk.
The evolution of advanced analytics technologies and the continual development of new analytical methodologies can help to optimize financial results, enable systems and services based on machine learning, obviate or mitigate fraud and reduce cybersecurity risks, among many other things.
Caserta Concepts, Zementis, and guest speaker from FICO presented the strategies, technologies and use cases driving predictive analytics in a big data environment.
For more information, visit www.casertaconcepts.com or contact us at info@casertaconcepts.com
Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.[2] Though used sometimes loosely partly due to a lack of formal definition, the best interpretation is that it is a large body of information that cannot be comprehended when used in small amounts only.[Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.[2] Though used sometimes loosely partly due to a lack of formal definition, the best interpretation is that it is a large body of information that cannot be comprehended when used in small amounts only.[Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.[2] Though used sometimes loosely partly due to a lack of formal definition, the best interpretation is that it is a large body of information that cannot be comprehended when used in small amounts only.[Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.[2] Though used sometimes loosely partly due to a lack of formal definition, the best interpretation is that it is a large body of information that cannot be comprehended when used in small amounts only.[Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.[2] Though used sometimes loosely partly due to a lack of formal definition, the best interpretation is that it is a large body of information that cannot be comprehended when used in small amounts only.[Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.[2] Though used sometimes loosely partly due to a lack of formal definition, the best interpretation is that it is a large body of informa
Italy Agriculture Equipment Market Outlook to 2027harveenkaur52
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