Modern IoT operations can drive digital transformation by analyzing the unprecedented amounts of data generated from devices and sensors in real-time.
Apache Spark is a widely used stream processing engine for real-time IoT applications. Spark streaming offers a rich set of APIs in the areas of ingestion, cloud integration, multi-source joins, blending streams with static data, time-window aggregations, transformations, data cleansing, and strong support for machine learning and predictive analytics.
Join Anand Venugopal, AVP & Business Head, StreamAnalytix and Sameer Bhide, Senior Solutions Architect, StreamAnalytix to learn about the rapid development and operationalization of real-time IoT applications covering an end-to-end flow of ingest, insight, action, and feedback.
The webinar will cover the following:
Generic IoT application blueprint
Case studies on IoT applications built on Apache Spark – connected car and industrial IoT
Demonstration of an easy, visual approach to building IoT Spark apps
Accelerating Digital Transformation with App ModernizationDavid J Rosenthal
Delivering a competitive edge with data and AI
Using the power of advanced analytics, machine learning, and AI, we can derive insights to help us optimize operations, drive innovation, and deliver value to the company and its customers.
Unite the data
Unlock the power of AI by consolidating data from different systems, technologies, and locations into data estates to enable broader connections and insights.
Inform decisions through visualized data
Use data to influence every decision with dashboards that intuitively visualize data, facilitate deeper analysis, and inform decisions.
Unleash insights with machine learning
Bring the power of advanced analytics, machine learning, and AI to derive insights from data. These insights bring increased value to organizations by optimizing operations and facilitating the development of more innovative products and services.
Embrace intelligent agents
Build intelligent agents that give employees the information and help they need when they need it— empowering employees to do more while streamlining operations.
Introduction to Time Series Analytics with Microsoft AzureCodit
Improve operations and decision-making by using real-time data insights and interactive analytics to accelerate IoT data use throughout your organization.
Discover the webinar here: https://bit.ly/38sMcrP
Event Streaming Architecture for Industry 4.0 - Abdelkrim Hadjidj & Jan Kuni...Flink Forward
New use cases under the Industry 4.0 umbrella are playing a key role in improving factory operations, process optimization, cost reduction and quality improvement. We propose an event streaming architecture to streamline the information flow all the way from the factory to the main data center. Building such a streaming architecture enables a manufacturer to react faster to critical operational events. However, it presents two main challenges:
Data acquisition in real time: data should be collected regardless of its location or access challenges are. It is commonplace to ingest data from hundreds of heterogeneous data sources (ERP, MES, Sensors, maintenance systems, etc).
Event processing in real time: events collected from different parts of the organization should be combined into actionable insights in real time. This is extremely challenging in a context where events can be lost or delayed.
In this talk, we show how Apache NiFi and MiNiFi can be used to collect a wide range of datasources in real-time, connecting the industrial and information worlds. Then, we show how Apache Flink’s unique features enables us to make sense of this data. For instance, we will explain how Flink’s time management such Event Time mode, late arrival handling and watermark mechanism can be used to address the challenge of processing IoT data originating from geographically distributed plants. Finally, we demonstrate an end to end streaming architecture for Industry 4.0 based on the Cloudera DataFlow platform.
Life occurs in real-time, and not surprisingly, more solutions are being built using streaming technologies. Event-based architectures are becoming the norm, and customers are expecting immediate access to their data. This new world offers many exciting opportunities, but also some new challenges. What do you do when your streaming data is not complete? What if it relies on another data source? Does the dependent data exist yet, and does it come from a 3rd party? How do we merge a complete picture of data when data is sourcing from multiple places at the same time? A new norm in the world of distributed services. Join us as we dive deep into the technical details around these scenarios and more. Expect to learn about stream-stream joins, enriching stream data using local or remote data, and ways to anticipate and correct errors within the stream. Leave with a better understanding of managing data dependencies within a Spark Structured Streaming application.
Accelerating Digital Transformation with App ModernizationDavid J Rosenthal
Delivering a competitive edge with data and AI
Using the power of advanced analytics, machine learning, and AI, we can derive insights to help us optimize operations, drive innovation, and deliver value to the company and its customers.
Unite the data
Unlock the power of AI by consolidating data from different systems, technologies, and locations into data estates to enable broader connections and insights.
Inform decisions through visualized data
Use data to influence every decision with dashboards that intuitively visualize data, facilitate deeper analysis, and inform decisions.
Unleash insights with machine learning
Bring the power of advanced analytics, machine learning, and AI to derive insights from data. These insights bring increased value to organizations by optimizing operations and facilitating the development of more innovative products and services.
Embrace intelligent agents
Build intelligent agents that give employees the information and help they need when they need it— empowering employees to do more while streamlining operations.
Introduction to Time Series Analytics with Microsoft AzureCodit
Improve operations and decision-making by using real-time data insights and interactive analytics to accelerate IoT data use throughout your organization.
Discover the webinar here: https://bit.ly/38sMcrP
Event Streaming Architecture for Industry 4.0 - Abdelkrim Hadjidj & Jan Kuni...Flink Forward
New use cases under the Industry 4.0 umbrella are playing a key role in improving factory operations, process optimization, cost reduction and quality improvement. We propose an event streaming architecture to streamline the information flow all the way from the factory to the main data center. Building such a streaming architecture enables a manufacturer to react faster to critical operational events. However, it presents two main challenges:
Data acquisition in real time: data should be collected regardless of its location or access challenges are. It is commonplace to ingest data from hundreds of heterogeneous data sources (ERP, MES, Sensors, maintenance systems, etc).
Event processing in real time: events collected from different parts of the organization should be combined into actionable insights in real time. This is extremely challenging in a context where events can be lost or delayed.
In this talk, we show how Apache NiFi and MiNiFi can be used to collect a wide range of datasources in real-time, connecting the industrial and information worlds. Then, we show how Apache Flink’s unique features enables us to make sense of this data. For instance, we will explain how Flink’s time management such Event Time mode, late arrival handling and watermark mechanism can be used to address the challenge of processing IoT data originating from geographically distributed plants. Finally, we demonstrate an end to end streaming architecture for Industry 4.0 based on the Cloudera DataFlow platform.
Life occurs in real-time, and not surprisingly, more solutions are being built using streaming technologies. Event-based architectures are becoming the norm, and customers are expecting immediate access to their data. This new world offers many exciting opportunities, but also some new challenges. What do you do when your streaming data is not complete? What if it relies on another data source? Does the dependent data exist yet, and does it come from a 3rd party? How do we merge a complete picture of data when data is sourcing from multiple places at the same time? A new norm in the world of distributed services. Join us as we dive deep into the technical details around these scenarios and more. Expect to learn about stream-stream joins, enriching stream data using local or remote data, and ways to anticipate and correct errors within the stream. Leave with a better understanding of managing data dependencies within a Spark Structured Streaming application.
In many database applications we first log data and then, a few hours or days later, we start analyzing it. But in a world that’s moving faster and faster, we sometimes need to analyze what is happening NOW.
Azure Stream Analytics allows you to analyze streams of data via a new Azure service. In this session you will see how to get started using this new service. From event hubs on the input side over temporal SQL queries: the demo’s in this session will show you end to end how to get started with Azure Stream Analytics.
Deep Learning Image Processing Applications in the EnterpriseGanesan Narayanasamy
The presentation has many use cases covering the following Image classification: "The process of identifying and detecting an object or a feature in a digital image or video," the report states. In retail, deep learning models "quickly scan and analyze in-store imagery to intuitively determine inventory movement."
Voice recognition: "The ability to receive and interpret dictation or to understand and carry out spoken commands. Models are able to convert captured voice commands to text and then use natural language processing to understand what is being said and in what context." In transportation, deep learning "uses voice commands to enable drivers to make phone calls and adjust internal controls - all without taking their hands off the steering wheel."
Anomaly detection: "Deep learning technique strives to recognize abnormal patterns which don't match the behaviors expected for a particular system, out of millions of different transactions. These applications can lead to the discovery of an attack on financial networks, fraud detection in insurance filings or credit card purchases, even isolating sensor data in industrial facilities signifying a safety issue."
Recommendation engines: "Analyze user actions in order to provide recommendations based on user behavior."
Sentiment analysis: "Leverages deep learning-heavy techniques such as natural language processing, text analysis, and computational linguistics to gain clear insight into customer opinion, understanding of consumer sentiment, and measuring the impact of marketing strategies."
Video analysis: "Process and evaluate vast streams of video footage for a range of tasks including threat detection, which can be used in airport security, banks, and sporting events."
Real-time analytics in IoT by Sam Vanhoutte (@Building The Future 2019)Codit
The number of IoT devices that streams data to a connected cloud backend increases daily. This data creates new possibilities for real-time analytics and can fundamentally change how our world works. In this presentation, you’ll learn how to build an Azure IoT architecture that is ready for real-time data analytics. Sam will demonstrate how data can be ingested and how different Azure technologies can be applied to achieve real-time intelligence. You’ll also discover how Azure Stream Analytics can be used to run streaming queries in the Cloud and on the Edge. By the end of this session you’ll have an understanding on how Azure Time Series Insights works to set up a Real Time data exploration, and you’ll get a glimpse of Azure Databricks for more advanced data analytics scenarios. Finally, you’ll learn how to deploy custom code to detect and act upon events in the data.
Successful AI/ML Projects with End-to-End Cloud Data EngineeringDatabricks
Trusted, high-quality data and efficient use of data engineers’ time are critical success factors for AI/ML projects. Enterprise data is complex—it comes from several sources, in a variety of formats, and at varied speeds. For your machine learning projects on Apache Spark, you need a holistic approach to data engineering: finding & discovering, ingesting & integrating, server-less processing at scale, and data governance. Stop by this session for an overview on how to set up AI/ML projects for success while Informatica takes the heavy lifting out of your data engineering.
IoT Suite is an enterprise grade solution that allows you to get started quickly through a set of extensible pre-configured solutions for common IoT scenarios
How a Media Data Platform Drives Real-time Insights & Analytics using Apache ...Databricks
Roularta is a leading publishing company in Belgium. As digital news and channels move at a rapid pace and contain massive volumes of data, Roularta decided in 2019 to invest in a Spark-based data platform to drive true real-time website analytics and unlock insights on previously untouched (big) data sources. In this talk we’ll first explain why and how Roularta embarked from a classical data warehouse to a Spark-based Lakehouse using Delta. We’ll outline the series of publishing & marketing use-cases done in the last 12 months and highlight for each use-case the advantages of Spark and how the team further tuned performance to truly deliver insights with high velocity.
Extending Operations from On-premises Solutions Towards Hybrid and Cloud - Da...Codit
Danny highlights the importance how a solid support system behind your chosen integration solution is not an option but a must if you want to get ahead and stay ahead. You’ll gain key insights into how managing and supporting an Azure platform is different from an on-premises solution and why you need experts who know the difference and have the resources to support you. In this session you’ll discover how a hybrid integration solution is the key to a strong stack, and why Azure is not the end of the story.
When you look at traditional ERP or management systems, they are usually used to manage the supply chain originating from either the point of Origin or point of destination which all our primarily physical locations. And for these, you have several processes like order to cash, source to pay, physical distribution, production etc.
Activeeon technology for Big Compute and cloud migrationActiveeon
Activeeon is a key technology provider and actor in the cloud migration. Activeeon offers software and middleware solutions for Big Compute, workload automation and HPC. The company also provides workflows solutions for Machine Learning & IA.
Get exclusive insights on IoT technology that has the potential to accelerate your business and give you the necessary agility to keep up with the pace of business. Join us and learn about the current and future state of the IoT landscape and what it takes to be successful in IoT. Gain insights from customer stories and discover how to get started building successful IoT solutions with Microsoft Azure.
Discover the webcast: https://bit.ly/2U1N8iI
Watch this recorded demonstration of SnapLogic from our team of experts who answer your hybrid cloud and big data integration questions.
demo, ipaas, elastic integration, cloud data, app integration, data integration, hybrid could integration, big data, big data integration
Next Generation of Data Integration with Azure Data Factory by Tom KerkhoveCodit
In this presentation, you'll learn the basics of Azure Data Factory. Tom Kerkhove will show you how you can take the data you have stored in your on-premise and cloud-based systems and create, manage and operate your own data pipelines.
In many database applications we first log data and then, a few hours or days later, we start analyzing it. But in a world that’s moving faster and faster, we sometimes need to analyze what is happening NOW.
Azure Stream Analytics allows you to analyze streams of data via a new Azure service. In this session you will see how to get started using this new service. From event hubs on the input side over temporal SQL queries: the demo’s in this session will show you end to end how to get started with Azure Stream Analytics.
Deep Learning Image Processing Applications in the EnterpriseGanesan Narayanasamy
The presentation has many use cases covering the following Image classification: "The process of identifying and detecting an object or a feature in a digital image or video," the report states. In retail, deep learning models "quickly scan and analyze in-store imagery to intuitively determine inventory movement."
Voice recognition: "The ability to receive and interpret dictation or to understand and carry out spoken commands. Models are able to convert captured voice commands to text and then use natural language processing to understand what is being said and in what context." In transportation, deep learning "uses voice commands to enable drivers to make phone calls and adjust internal controls - all without taking their hands off the steering wheel."
Anomaly detection: "Deep learning technique strives to recognize abnormal patterns which don't match the behaviors expected for a particular system, out of millions of different transactions. These applications can lead to the discovery of an attack on financial networks, fraud detection in insurance filings or credit card purchases, even isolating sensor data in industrial facilities signifying a safety issue."
Recommendation engines: "Analyze user actions in order to provide recommendations based on user behavior."
Sentiment analysis: "Leverages deep learning-heavy techniques such as natural language processing, text analysis, and computational linguistics to gain clear insight into customer opinion, understanding of consumer sentiment, and measuring the impact of marketing strategies."
Video analysis: "Process and evaluate vast streams of video footage for a range of tasks including threat detection, which can be used in airport security, banks, and sporting events."
Real-time analytics in IoT by Sam Vanhoutte (@Building The Future 2019)Codit
The number of IoT devices that streams data to a connected cloud backend increases daily. This data creates new possibilities for real-time analytics and can fundamentally change how our world works. In this presentation, you’ll learn how to build an Azure IoT architecture that is ready for real-time data analytics. Sam will demonstrate how data can be ingested and how different Azure technologies can be applied to achieve real-time intelligence. You’ll also discover how Azure Stream Analytics can be used to run streaming queries in the Cloud and on the Edge. By the end of this session you’ll have an understanding on how Azure Time Series Insights works to set up a Real Time data exploration, and you’ll get a glimpse of Azure Databricks for more advanced data analytics scenarios. Finally, you’ll learn how to deploy custom code to detect and act upon events in the data.
Successful AI/ML Projects with End-to-End Cloud Data EngineeringDatabricks
Trusted, high-quality data and efficient use of data engineers’ time are critical success factors for AI/ML projects. Enterprise data is complex—it comes from several sources, in a variety of formats, and at varied speeds. For your machine learning projects on Apache Spark, you need a holistic approach to data engineering: finding & discovering, ingesting & integrating, server-less processing at scale, and data governance. Stop by this session for an overview on how to set up AI/ML projects for success while Informatica takes the heavy lifting out of your data engineering.
IoT Suite is an enterprise grade solution that allows you to get started quickly through a set of extensible pre-configured solutions for common IoT scenarios
How a Media Data Platform Drives Real-time Insights & Analytics using Apache ...Databricks
Roularta is a leading publishing company in Belgium. As digital news and channels move at a rapid pace and contain massive volumes of data, Roularta decided in 2019 to invest in a Spark-based data platform to drive true real-time website analytics and unlock insights on previously untouched (big) data sources. In this talk we’ll first explain why and how Roularta embarked from a classical data warehouse to a Spark-based Lakehouse using Delta. We’ll outline the series of publishing & marketing use-cases done in the last 12 months and highlight for each use-case the advantages of Spark and how the team further tuned performance to truly deliver insights with high velocity.
Extending Operations from On-premises Solutions Towards Hybrid and Cloud - Da...Codit
Danny highlights the importance how a solid support system behind your chosen integration solution is not an option but a must if you want to get ahead and stay ahead. You’ll gain key insights into how managing and supporting an Azure platform is different from an on-premises solution and why you need experts who know the difference and have the resources to support you. In this session you’ll discover how a hybrid integration solution is the key to a strong stack, and why Azure is not the end of the story.
When you look at traditional ERP or management systems, they are usually used to manage the supply chain originating from either the point of Origin or point of destination which all our primarily physical locations. And for these, you have several processes like order to cash, source to pay, physical distribution, production etc.
Activeeon technology for Big Compute and cloud migrationActiveeon
Activeeon is a key technology provider and actor in the cloud migration. Activeeon offers software and middleware solutions for Big Compute, workload automation and HPC. The company also provides workflows solutions for Machine Learning & IA.
Get exclusive insights on IoT technology that has the potential to accelerate your business and give you the necessary agility to keep up with the pace of business. Join us and learn about the current and future state of the IoT landscape and what it takes to be successful in IoT. Gain insights from customer stories and discover how to get started building successful IoT solutions with Microsoft Azure.
Discover the webcast: https://bit.ly/2U1N8iI
Watch this recorded demonstration of SnapLogic from our team of experts who answer your hybrid cloud and big data integration questions.
demo, ipaas, elastic integration, cloud data, app integration, data integration, hybrid could integration, big data, big data integration
Next Generation of Data Integration with Azure Data Factory by Tom KerkhoveCodit
In this presentation, you'll learn the basics of Azure Data Factory. Tom Kerkhove will show you how you can take the data you have stored in your on-premise and cloud-based systems and create, manage and operate your own data pipelines.
Powering the Internet of Things with Apache HadoopCloudera, Inc.
Without the right data management strategy, investments in Internet of Things (IoT) can yield limited results. Apache Hadoop has emerged as a key architectural component that can help make sense of IoT data, enabling never before seen data products and solutions.
Vertex perspectives ai optimized chipsets (part i)Yanai Oron
Businesses are increasingly adopting AI to create new applications to transform existing operations, driving big data with the growth of IoT and 5G networks and increasing future process complexities for human operators. In this new environment, AI will be needed to write algorithms dynamically to automate the entire programming process. Fortunately, algorithms associated with deep learning are able to achieve enhanced performance with increasing data, unlike the rest associated with machine learning.
Vertex Perspectives | AI-optimized Chipsets | Part IVertex Holdings
Businesses are increasingly adopting AI to create new applications to transform existing operations, driving big data with the growth of IoT and 5G networks and increasing future process complexities for human operators. In this new environment, AI will be needed to write algorithms dynamically to automate the entire programming process. Fortunately, algorithms associated with deep learning are able to achieve enhanced performance with increasing data, unlike the rest associated with machine learning. To date, deep learning technology has primarily been a software play. Existing processors were not originally designed for these new applications. Hence the need to develop AI-optimized hardware.
Sap Leonardo - what is it, and why would I want one?Tom Raftery
A quick run through of the technologies running through SAP's Innovation portfolio of products, called SAP Leonardo, and use cases where it has been deployed successfully with customers
Using AWS IoT for Industrial Applications - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Understand how AWS IoT can be used for Industrial Applications including predictive quality, asset condition monitoring, and predictive maintenance
- Know how features of AWS IoT Core such as the rules engine, device shadow, and message broker are used for industrial applications
- Articulate how AWS IoT Analytics supports machine learning
Subscribed 2015: The Explosion of Smart Connected ThingsZuora, Inc.
Market experts and leading analyst firms predict that we’ll see the number of smart connected things grow from ~2B today to over 50B within the coming decade. The introduction of these new smart, connected things enable functional variability to shift from the physical design to the digital smarts that are being embedded in the “thing”. This explosion of smart, connected things provides both their creators and users (consumers and enterprises) with endless opportunities to continually monetize features, options, usage and data over the lifetime of the “thing’s” operation. Come to this session and see how PTC and Zuora are helping businesses capitalize on these important new revenue streams through a live demo.
The fastest way to convert etl analytics and data warehouse to AWS- Impetus W...Impetus Technologies
Amazon Web Services (AWS) delivers next-gen information architecture to quickly deploy end-to-end cloud analytics and data warehousing solutions.
As an AWS Advanced Consulting Partner, Impetus offers an ML-based automated solution for data warehouse, ETL, and analytics workload transformation to AWS. You can convert almost all legacy sources to an AWS-native technology stack (Redshift-EMR, Glue, Lambda, etc.) 4x faster than hand-coding.
Join us as we share our experience in delivering continued value to Fortune 1000 companies.
The webinar will detail the following:
Critical considerations for moving to AWS
A strategy for transforming workloads to AWS
Nuances of workload conversion to AWS
A demo of the fastest way to convert ETL, analytics, and data warehouse to AWS
To learn more - https://bit.ly/2RHJeac
Eliminate cyber-security threats using data analytics – Build a resilient ent...Impetus Technologies
The current pandemic situation has fueled an unprecedented rise in digital transactions across the globe. This has led to a surge in cyber attacks and malicious online activities. To ensure business continuity and mitigate risks, enterprises need to detect and respond to security threats in real-time.
While the “new normal” presents several security challenges, it also offers enterprises a unique opportunity to enforce and bolster 360-degree security measures. Join our upcoming webinar to discover how advanced data analytics can help you detect and address:
Fraudulent transactions
Cyber attacks
Data thefts
Asset and data security
To learn more about the webinar - view https://bit.ly/3hQ4sgw
Automated EDW Assessment and Actionable Recommendations - Impetus WebinarImpetus Technologies
Assessing analytical workloads is the first step towards successful cloud migration. However, an assessment typically provides a non-actionable list of inventories.
An intelligent automation-based workload assessment offered by Impetus’ Workload Transformation Solution can help you get actionable insights. It profiles workloads and maps their compatibility with your target cloud environment. As a result, you are prepared to avoid common pitfalls and ensure a successful cloud transition of your ETL and analytics workloads.
In this session, our experts will share insights on how this solution can help you:
Identify workload complexities, patterns, and technical debt
Map existing workloads to your target cloud stack
Create a blueprint for future-state architecture based on an automation-based intelligent assessment
Implement best practices to de-risk your cloud transition
We will also share success stories of how Impetus has helped Fortune 500 enterprises make the right decisions for a seamless EDW transformation.
To learn more view our webinar here - https://bit.ly/37zSwML
While enterprises are increasingly adopting a cloud strategy, a successful cloud journey requires a mature foundation built through a well-architected, phased, and incremental approach.
This webinar will help you plan for a mature foundation for all the phases of a cloud adoption journey.
The content of this session is drawn from multiple cloud adoption projects at large enterprises to detail the following:
Opportunities and challenges of cloud adoption
Best practices to ensure a seamless transition to the cloud including exploration, migration, basic patterns, application transformation and maintenance
Automation and self-service enablement in the cloud.
View the webinar here - https://bit.ly/2TcSkMv
Best practices to build a sustainable data lake on cloud - Impetus WebinarImpetus Technologies
Creating new data lakes or migrating existing ones on the cloud has been a de-facto trend. With reduced costs and agile infrastructure, it is easier to derive business value on the cloud.
While there are so many choices for architecture, tech stack, and solutions available on various platforms to fit your use case, it is imperative to consider the key mantras and lifeline principles that will help you succeed in your cloud data lake journey.
In this webinar, we share the best practices and the key considerations that will help you build a robust data lake architecture on the cloud.
To view the webinar - visit https://bit.ly/2sG8BAp
View the webinar here - https://bit.ly/2ErkxYY
Enterprises are moving their data warehouse to the cloud to take advantage of reduced operational and administrative overheads, improved business agility, and unmatched simplicity.
The Impetus Workload Transformation Solution makes the journey to the cloud easier by automating the DW migration to cloud-native data warehouse platforms like Snowflake. The solution enables enterprises to automate conversion of source DDL, DML scripts, business logic, and procedural constructs. Enterprises can preserve their existing investments, eliminate error-prone, slow, and expensive manual practices, mitigate any risk, and accelerate time-to-market with the solution.
Join our upcoming webinar where Impetus experts will detail:
Cloud migration strategy
Critical considerations for moving to the cloud
Nuances of migration journey to Snowflake
Demo – Automated workload transformation to Snowflake.
To view - visit https://bit.ly/2ErkxYY
Instantly convert Teradata ETL and EDW to Spark- Impetus webinarImpetus Technologies
Teradata has been a popular choice for analytical data processing. As data complexity increases, more and more enterprises need the flexibility and scalability advantages of ‘modern data architecture’ technologies. The sophisticated algorithmic and statistical analytics, and interoperability, would also enable businesses to handle vast and complex data at high speeds.
But the challenges and risks involved in an enterprise data warehouse transformation are well known. Impetus Workload Transformation solution is the only 100% cloud ready solution to assess, migrate, and validate your complex data transformation and analytical workloads from Teradata to modern open systems such as Spark, Hive or Scala. Find out how your enterprise can ensure a swift move to big data / cloud using an automation based EDW transformation solution.
Join our upcoming webinar where our modern data architecture experts will talk about:
Using automation to identify the candidate workloads to migrate
Building a prioritized EDW conversion roadmap based on cost, performance, and business objectives
Devising an effective offload strategy to a scalable data architecture
Rapidly transforming complex business logic and code (with UDFs) using automation.
To view the webinar, visit - https://bit.ly/347S19N
Keys to establish sustainable DW and analytics on the cloud -Impetus webinarImpetus Technologies
Cloud adoption is inevitable to implement digital transformation and drive customer expectations. For a seamless transition of your analytical layer to modern cloud platforms, powerful automation is crucial at a process, code, and data level. However, this transition is not the end, but the beginning of creating sustainable business outcomes through a democratized and extensible data architecture. These outcomes are realized through the cloud architecture’s scaling options and the ease of advanced analytics, establishing a data-driven culture, and monetizing data for increased/new revenue.
Join our upcoming webinar where our experts share:
Keys to formulating an effective blueprint for DW transformation to the cloud
How to leverage existing DW investments by harnessing decades of coding effort within weeks
The best practices for driving tangible business outcomes through cloud-scale analytics.
To view the webinar, visit - https://bit.ly/2MJ01rK
Enterprises looking to modernize the data warehouse have been facing the dilemma of choosing between migration of legacy systems and total re-engineering. While migrating the systems as is is not the best transformation choice for all workloads, total re-engineering can be complex and disrupt business processes.
An EDW transformation approach that maintains a balance between the two extreme approaches is required to solve this problem. You will have full control in addressing low-performing or costly, resource-constrained workloads, while also building a strong foundation for a modern data warehouse architecture that supports sophisticated analytics using a cloud/hybrid/on-premises strategy.
In this webinar, experts from Impetus will walk you through a path to solve this dilemma. Attendees of this session will be able to:
Arrive at a pragmatic decision when faced with the dilemma
Learn methods to estimate and measure the bottom-line impact across applications and reporting workloads
Build a prioritized transformation roadmap of use cases based on cost, performance, and business needs
Understand how automation can be used to optimize workloads for a scalable and iterative architecture.
To view the webinar visit - https://bit.ly/31KWqO6
Organizations are collecting massive amounts of data from disparate sources. However, they continuously face the challenge of identifying patterns, detecting anomalies, and projecting future trends based on large data sets. Machine learning for anomaly detection provides a promising alternative for the detection and classification of anomalies.
Find out how you can implement machine learning to increase speed and effectiveness in identifying and reporting anomalies.
In this webinar, we will discuss :
How machine learning can help in identifying anomalies
Steps to approach an anomaly detection problem
Various techniques available for anomaly detection
Best algorithms that fit in different situations
Implementing an anomaly detection use case on the StreamAnalytix platform
To view the webinar - https://bit.ly/2IV2ahC
Keys to Formulating an Effective Data Management Strategy in the Age of DataImpetus Technologies
Companies who are leaders in the digital transformation revolution have mastered how they manage data. In order to compete with them, it is important that all businesses have a comprehensive data management strategy. A solid data management strategy should address the needs of all areas of business – from operations, marketing, and sales to service, finance, and more. But how can enterprises formulate a strategy that provides all business functions with quick and complete access to the data, analytics, and machine learning that they need, both now and in the future?
In this webinar our guest speaker Mike Gualtieri, VP & Principal Analyst, Forrester Research and Larry Pearson, VP Strategic Alliances, Impetus Technologies will outline the fundamental building blocks of such a strategy.
To view the webinar, https://bit.ly/31jyLnV
StreamAnalytix offers the must-haves of a modern ETL solution and the ability to accelerate your shift to the cloud. With an intuitive visual interface, StreamAnalytix simplifies building and running Spark-based ETL workflows on the cloud.
View our latest webinar where you’ll learn about:
The must-haves of a modern ETL solution on cloud
How you can accelerate application development and minimize hand coding
How to deploy a cloud-based ETL application in minutes
Smarter ways to operationalize your ETL workflow
You can also view the webinar here - https://www.streamanalytix.com/webinar/build-spark-based-etl-workflows-on-cloud-in-minutes/
Planning your Next-Gen Change Data Capture (CDC) Architecture in 2019 - Strea...Impetus Technologies
Traditional databases and batch ETL operations have not been able to serve the growing data volumes and the need for fast and continuous data processing.
How can modern enterprises provide their business users real-time access to the most up-to-date and complete data?
In our upcoming webinar, our experts will talk about how real-time CDC improves data availability and fast data processing through incremental updates in the big data lake, without modifying or slowing down source systems. Join this session to learn:
What is CDC and how it impacts business
The various methods for CDC in the enterprise data warehouse
The key factors to consider while building a next-gen CDC architecture:
Batch vs. real-time approaches
Moving from just capturing and storing, to capturing enriching, transforming, and storing
Avoiding stopgap silos to state-through processing
Implementation of CDC through a live demo and use-case
You can view the webinar here - https://www.streamanalytix.com/webinar/planning-your-next-gen-change-data-capture-cdc-architecture-in-2019/
For more information visit - https://www.streamanalytix.com
Apache Spark – The New Enterprise Backbone for ETL, Batch Processing and Real...Impetus Technologies
In spite of investments in big data lakes, there is wide use of expensive proprietary products for data ingestion, integration, and transformation (ETL) while bringing and processing data on the lake.
Enterprises have successfully tested Apache Spark for its versatility and strengths as a distributed computing framework that can completely handle all needs for data processing, analytics, and machine learning workloads.
Since the Hadoop distributions and the public cloud already include Apache Spark, there is nothing new to be procured. However, the skills required to put Spark to good use are typically unavailable today.
In this webinar, we will discuss how Apache Spark can be an inexpensive enterprise backbone for all types of data processing workloads. We will also demo how a visual framework on top of Apache Spark makes it much more viable.
The following scenarios will be covered:
On-Prem
Data quality and ETL with Apache Spark using pre-built operators
Advanced monitoring of Spark pipelines
On Cloud
Visual interactive development of Apache Spark Structured Streaming pipelines
IoT use-case with event-time, late-arrival and watermarks
Python based predictive analytics running on Spark
Anomaly Detection - Real World Scenarios, Approaches and Live ImplementationImpetus Technologies
Detecting anomalous patterns in data can lead to significant actionable insights in a wide variety of application domains, such as fraud detection, network traffic management, predictive healthcare, energy monitoring and many more.
However, detecting anomalies accurately can be difficult. What qualifies as an anomaly is continuously changing and anomalous patterns are unexpected. An effective anomaly detection system needs to continuously self-learn without relying on pre-programmed thresholds.
Join our speakers Ravishankar Rao Vallabhajosyula, Senior Data Scientist, Impetus Technologies and Saurabh Dutta, Technical Product Manager - StreamAnalytix, in a discussion on:
Importance of anomaly detection in enterprise data, types of anomalies, and challenges
Prominent real-time application areas
Approaches, techniques and algorithms for anomaly detection
Sample use-case implementation on the StreamAnalytix platform
The structured streaming upgrade to Apache Spark and how enterprises can bene...Impetus Technologies
The adoption of Apache Spark to analyze data in real-time is increasing with its ability to handle sophisticated analytical requirements and a common framework for streaming and batch. However, most organizations are also looking for "true streaming" features like lower latency and the ability to process out-of-order data.
Structured Streaming, a new high-level API, introduced in Apache Spark 2.0 promises these and other enhancements to the Spark approach to streaming data processing.
In this webinar, Anand Venugopal (Product Head) and other technical experts from StreamAnalytix, speak about the promising developments in Apache Spark 2.0 and how organizations can leverage structured streaming to make timely and accurate decisions and stay competitive.
Apache spark empowering the real time data driven enterprise - StreamAnalytix...Impetus Technologies
Apache Spark is one of the most popular Big Data frameworks today. It is fast becoming the de facto technology choice for stream processing, real-time analytics, data science and machine learning applications at scale. It has moved well beyond the early-adopter phase, is supported by a vibrant open source community and is enjoying accelerated adoption in enterprises.
Join our guest speaker from Forrester Research, VP & Principal Analyst, Mike Gualtieri and StreamAnalytix, Product Head, Anand Venugopal for a discussion on the trends and directions defining the growing importance of Apache Spark for stream processing, machine learning and other advanced data analytics applications.
Anomaly Detection and Spark Implementation - Meetup Presentation.pptxImpetus Technologies
StreamAnalytix sponsored a meetup on “Anomaly Detection Techniques and Implementation using Apache Spark” which took place on Tuesday December 5, 2017 at Larkspur Landing Milpitas Hotel, Milpitas, CA. The meetup was led by Maxim Shkarayev, Lead Data Scientist, Impetus Technologies along with Punit Shah, Solution Architect, StreamAnalytix and Anand Venugopal, Product Head & AVP, StreamAnalytix, who introduced and summarized the vast field of Anomaly Detection and its applications in various industry problems. The speakers at the event also offered a structured approach to choose the right anomaly detection techniques based on specific use-cases and data characteristics which was followed by a demonstration of some real-world anomaly detection use-cases on Apache Spark based analytics platform.
StreamAnalytix is a software platform that enables enterprises to analyze and respond to events in real-time at Big Data scale. It is designed to rapidly build and deploy streaming analytics applications for any industry vertical, any data format, and any use case.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
4. IOT Market: Gartner Prediction
7.6 Billion
Things that will ship in 2021
32% CAGR
End point growth rate 2016-2021
25.1 Billion
Units install base in 2021
$3.9 Trillion
Total spending: 2021
end points and services
5. Smart Home Smart Car Smart Building Smart City Smart Agriculture
Smart Factory Smart Healthcare Smart Data Center Smart Energy Smart Retail
IoT – Market Sub-Domains
6. IoT Solution Architecture and the Role of Spark
Field IoT
Gateway
Cloud IoT
Integration HUB
Field IoT Gateway
Connected
Things
Enterprise
Applications
Centralized IoT Data Mgmt.
& Analytics Platform
10. Managed Devices
EDA / SEDA Sources
IOT Application Architecture: Conceptual Layers
Management LayerIoT Gateway Data Ingestion
Data Processing
& Storage Layer
Insights Layer Action Layer
Security Data
Sources
Compute
Engine
(Spark)
ML - Model
Updates Patterns–
(A-B, Champion
Challenger etc…)
Notification
Services
Fault Tolerance Protocol
Support
Data Filtering,
Blending &
Enrichment
Rule Engine Alerts
State
Management
Ontology &
Metadata
Management
Structured
Query
Feedback
Loop
External
Services
Integration
Device Proxy Data
Persistence
Custom Business
Flows (IFTTT,
Lambda etc)
INGEST ENRICH ANALYZE ACT
Configuration &
Connection Management
Performance
Management
Application Life Cycle
Management
Version Updates
PaaS Integration Computer Infrastructure
SPARK
Infrastructure Layer
11. Spark as the IoT Compute Engine
| Massively scalable
| Rich set of transformations
| Industry adoption
| Unified & simplified programming model
| Support for machine learning
| Micro-batch capable – tending to NRT
12. Recommendations
| Adopt an integrated approach to IoT development
| Design a platform layer that can adopt to business’ dynamic needs
| Create a vendor neutral & interoperable architecture
| Adopt software products to quickly operationalize IoT use cases
16. Connected Car – Driver Risk Profiling
Brief Background
Leading insurance provider in the US
• Classify drivers based on current driving
pattern and historical data
• Raise alerts on behavior change
• Blend data from syndicated and open /
public data marts & services
• Derive additional analytics through
supplemental data flows
Business Need
To create an end-to-end analytics application
for driver profiling & RT risk assessment
17. Central
Aggregation
Server / Data
Flow
Manager
On-premise: Bare-Metal and/or VMs | Public / Hybrid Cloud
Data Center / Cloud
Storage and Offline Analytics
Device Provisioning and
Management (identity /
registration etc.)
Open Interfaces –
extensibility and
customizability in all
directions
Real Time Dash-boarding
Condition Monitoring
Predictive Maintenance
Smart Alerting
Root Cause Analytics
Closed-loop Feedback
Edge
Custom solution OR
3rd party IoT interface vendor
Data flow
Control flow
End Device 1
End Device 2
End Device 3
End Device 1
End Device 2
End Device 3
Smart Car 1
Smart Car 2
IoT / Connected Car Solution with StreamAnalytix
IOT data
interface
(MQTT / HTTP /
WebSockets)
IOT data
interface
(MQTT / HTTP /
WebSockets)
Gateway
18. AWS IoT
(Spark)
Ingestion Enrichment Analytics
Public Cloud Services /
Third-party Services
Dashboards
Handheld
Devices
Persistence
Automated Device
Installed in OBD-II Port
High Level Solution Overview
Alerts
20. Connected Car – Driver Risk Profiling
Ingest events using
AWS IoT gateway
Mask PII & enrich data with
external & historical sources
Score a ‘Risk Assessment’ model that uses
• Weather conditions
• Time of trip
• Hard brakes and acceleration
• Duration over 70mph
• Previous number of risk instances
Raise alerts based on
risk scores
Create RT and historical
dashboards
21. Industrial IoT Use Case – Device Health Monitoring
Brief Background
Leader in industrial automation, information,
and engineering services
• Various machine health parameters
collected in different timelines from an
array of sensors
• Compute and store correlation between
sensor data when a process parameter is
altered
• Leverage existing investments in Azure
cloud infrastructure
Business Need
Measure impact to process dynamics by
calculating correlation between various
sensor data
22. End-to-end solution deployed
on StreamAnalytix
Data pipelines used pre-built
components:
• Data ingestion
• Statistical functions
• Data enrichment
• Visualization
Cloud: Microsoft Azure
Source: Event Hub
Compute: Spark jobs on HDInsight
Orchestration: StreamAnalytix
High Level Solution Approach
Reporting
Dashboard
Manufacturing
Units
HD-Insights
23. • Ingest data from different MS Azure Event Hub sources
• Enrich incoming data
• Outer-join on incoming datasets
• Aggregate result data and group by plantIDs
• Post streaming results on WebSockets
Data Pipeline View
Industrial Automation - Turbine Data Analytics
24. Key Takeaways
| IoT capabilities in StreamAnalytix
• Data Sources : Azure Event Hub, AWS IoT, MQTT, Kinesis, S3
• Data Sink : Redshift, Hadoop, MQTT, S3, Kinesis, WebSockets
• PaaS Service Integration : SQS, Lambda, SNS
| Integrated approach to IoT development
| IoT applications are dynamic
| Vendor neutral & interoperable architecture
| COTS & open source offerings to quickly operationalize IoT use cases