The challenge of computing big data for evolving digital business processes demands variety of computation techniques and engines (SQL, OLAP, time-series, graph, document store), but working in unified framework. A simple architecture of data transformations while ensuring the security, governance, and operational administration are the necessary critical components for enterprise production environments supporting day-to-day business processes. In this session, you will learn about best practices & critical components to ensure business value from latest production deployments. Hear how existing customers are using SAP Vora and the value they have achieved so far with this in-memory engine for distributed data processing. The session provides you with a clear understanding how SAP Vora and open source components like Apache Hadoop and Apache Spark offer an architecture that supports a wide variety of use cases and industries. You will also receive very useful insight where to find development resources, test drive demos, and general documentation.
How to optimize Hortonworks Apache Spark ML workloads on Power - POWER 8/9 architecture is the latest offering from IBM and OpenPower foundation. It is the perfect platform for optimizing Hortonworks Spark's performance. During this presentation we will walk the audience through steps required to optimize YARN, HDFS, and Spark on a Power cluster.
Step required:
1) Classify workload into CPU, Memory, IO or mixed (CPU, memory, IO) intensive
2) Characterize "out-of-box" Hortonworks spark workload to understand CPU, Memory, IO and Network performance characteristics
3) Floor Plan cluster resources
4) Tune "out-of-box" workload to navigate "Roofline" Performance space in the above named dimensions
5) If workload is Memory / IO/ Network intensive bound then tune SPARK to increase operational intensity operations/byte as much as possible to make it CPU bound
6) Divide search space into regions and perform exhaustive search.
7) Identify Performance bottlenecks by resource monitoring and tune the System, JVM or application layer by profiling application and hardware counters if required.
As Hadoop applications move into cloud deployments, object stores become more and more the source and destination of data. But object stores are not filesystems: sometimes they are slower; security is different,
What are the secret settings to get maximum performance from queries against data living in cloud object stores? That's at the filesystem client, the file format and the query engine layers? It's even how you lay out the files —the directory structure and the names you give them.
We know these things, from our work in all these layers, from the benchmarking we've done —and the support calls we get when people have problems. And now: we'll show you.
This talk will start from the ground up "why isn't an object store a filesystem?" issue, showing how that breaks fundamental assumptions in code, and so causes performance issues which you don't get when working with HDFS. We'll look at the ways to get Apache Hive and Spark to work better, looking at optimizations which have been done to enable this —and what work is ongoing. Finally, we'll consider what your own code needs to do in order to adapt to cloud execution.
Realizing the Promise of Portable Data Processing with Apache BeamDataWorks Summit
The world of big data involves an ever changing field of players. Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms. In a way, Apache Beam is a glue that can connect the Big Data ecosystem together; it enables users to "run-anything-anywhere".
This talk will briefly cover the capabilities of the Beam model for data processing, as well as the current state of the Beam ecosystem. We'll discuss Beam architecture and dive into the portability layer. We'll offer a technical analysis of the Beam's powerful primitive operations that enable true and reliable portability across diverse environments. Finally, we'll demonstrate a complex pipeline running on multiple runners in multiple deployment scenarios (e.g. Apache Spark on Amazon Web Services, Apache Flink on Google Cloud, Apache Apex on-premise), and give a glimpse at some of the challenges Beam aims to address in the future.
Speaker
Davor Bonaci, Senior Software Engineer, Google
Dynamic DDL: Adding structure to streaming IoT data on the flyDataWorks Summit
At the end of day the only thing that data scientists want is one thing. They want tabular data for their analysis.
They do not want to spend hours or days preparing data. How does a data engineer handle the massive amount of data
that is being streamed at them from IoT devices and apps and at the same time add structure to it so that data scientists
can focus on finding insights and not preparing data? By the way, you need to do this within minutes (sometimes seconds).
Oh... and there are a bunch more data sources that you need to ingest and the current providers of data are changing their structure.
At GoPro, we have massive amounts of heterogeneous data being streamed at us from our consumer devices
and applications, and we have developed a concept of "dynamic DDL" to structure our streamed data on the fly using
Spark Streaming, Kafka, HBase, Hive, and S3. The idea is simple. Add structure (schema) to the data as soon as possible.
Allow the providers of the data to dictate the structure. And automatically create event-based and state-based tables (DDL)
for all data sources to allow data scientists to access the data via their lingua franca, SQL, within minutes.
Hadoop’s capabilities offer untapped potential for business insights but companies often get weighed down with DIY platforms and fail to keep up with the requirements. Join this Dell EMC session which will address this challenge with ready bundles to quickly deliver solutions for ETL offload, Single View, & IoT.
Get more value from your big data:
• Deploy big data applications faster
• Increase business agility
• Confidently deliver high performance and endless scale
• Improve IT operational efficiency
Speaker
Shawn Smith, Big Data Specialist, Dell EMC
Data Driving Yahoo Mail Growth and Evolution with a 50 PB Hadoop WarehouseDataWorks Summit
Yahoo Mail has 200+ million users a month and generates hundreds of terabytes of data per day, which continues to grow steadily. The nature of email messages has also evolved: for example, today the majority of them are generated by machines, consisting of newsletters, social media notifications, purchase invoices, travel bookings, and the like, which drove innovations in product development to help users organize their inboxes.
Since 2014, the Yahoo Mail Data Engineering team took on the task of revamping the Mail data warehouse and analytics infrastructure in order to drive the continued growth and evolution of Yahoo Mail. Along the way we have built a 50 PB Hadoop warehouse, and surrounding analytics and machine learning programs that have transformed the way data plays in Yahoo Mail.
In this session we will share our experience from this 3 year journey, from the system architecture, analytics systems built, to the learnings from development and drive for adoption.
Hadoop clusters can store nearly everything in a cheap and blazingly fast way to your data lake. Answering questions and gaining insights out of this ever growing stream becomes the decisive part for many businesses. Increasingly data has a natural structure as a graph, with vertices linked by edges, and many questions arising about the data involve graph traversals or other complex queries, for which one does not have an a priori given bound on the length of paths.
Spark with GraphX is great for answering relatively simple graph questions which are worth starting a Spark job for, because they essentially involve the whole graph. But does it make sense to start one for every ad-hoc query or is it suitable for complex real-time queries?
In this talk I will introduce an alternative solution that adds those features to an existing Hadoop/Spark setup and enables real-time insights. I will address the following topics:
* Challenges in gaining deeper insights from large amounts of graph data
* Benefits and limitations of graph analysis with Spark
* Introduction to ArangoDB SmartGraphs
* Deployment of Hadoop, Spark and ArangoDB using DC/OS
* Performing complex queries on billions of nodes and vertices leveraging ArangoDB SmartGraphs (Live Demo)
How to optimize Hortonworks Apache Spark ML workloads on Power - POWER 8/9 architecture is the latest offering from IBM and OpenPower foundation. It is the perfect platform for optimizing Hortonworks Spark's performance. During this presentation we will walk the audience through steps required to optimize YARN, HDFS, and Spark on a Power cluster.
Step required:
1) Classify workload into CPU, Memory, IO or mixed (CPU, memory, IO) intensive
2) Characterize "out-of-box" Hortonworks spark workload to understand CPU, Memory, IO and Network performance characteristics
3) Floor Plan cluster resources
4) Tune "out-of-box" workload to navigate "Roofline" Performance space in the above named dimensions
5) If workload is Memory / IO/ Network intensive bound then tune SPARK to increase operational intensity operations/byte as much as possible to make it CPU bound
6) Divide search space into regions and perform exhaustive search.
7) Identify Performance bottlenecks by resource monitoring and tune the System, JVM or application layer by profiling application and hardware counters if required.
As Hadoop applications move into cloud deployments, object stores become more and more the source and destination of data. But object stores are not filesystems: sometimes they are slower; security is different,
What are the secret settings to get maximum performance from queries against data living in cloud object stores? That's at the filesystem client, the file format and the query engine layers? It's even how you lay out the files —the directory structure and the names you give them.
We know these things, from our work in all these layers, from the benchmarking we've done —and the support calls we get when people have problems. And now: we'll show you.
This talk will start from the ground up "why isn't an object store a filesystem?" issue, showing how that breaks fundamental assumptions in code, and so causes performance issues which you don't get when working with HDFS. We'll look at the ways to get Apache Hive and Spark to work better, looking at optimizations which have been done to enable this —and what work is ongoing. Finally, we'll consider what your own code needs to do in order to adapt to cloud execution.
Realizing the Promise of Portable Data Processing with Apache BeamDataWorks Summit
The world of big data involves an ever changing field of players. Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms. In a way, Apache Beam is a glue that can connect the Big Data ecosystem together; it enables users to "run-anything-anywhere".
This talk will briefly cover the capabilities of the Beam model for data processing, as well as the current state of the Beam ecosystem. We'll discuss Beam architecture and dive into the portability layer. We'll offer a technical analysis of the Beam's powerful primitive operations that enable true and reliable portability across diverse environments. Finally, we'll demonstrate a complex pipeline running on multiple runners in multiple deployment scenarios (e.g. Apache Spark on Amazon Web Services, Apache Flink on Google Cloud, Apache Apex on-premise), and give a glimpse at some of the challenges Beam aims to address in the future.
Speaker
Davor Bonaci, Senior Software Engineer, Google
Dynamic DDL: Adding structure to streaming IoT data on the flyDataWorks Summit
At the end of day the only thing that data scientists want is one thing. They want tabular data for their analysis.
They do not want to spend hours or days preparing data. How does a data engineer handle the massive amount of data
that is being streamed at them from IoT devices and apps and at the same time add structure to it so that data scientists
can focus on finding insights and not preparing data? By the way, you need to do this within minutes (sometimes seconds).
Oh... and there are a bunch more data sources that you need to ingest and the current providers of data are changing their structure.
At GoPro, we have massive amounts of heterogeneous data being streamed at us from our consumer devices
and applications, and we have developed a concept of "dynamic DDL" to structure our streamed data on the fly using
Spark Streaming, Kafka, HBase, Hive, and S3. The idea is simple. Add structure (schema) to the data as soon as possible.
Allow the providers of the data to dictate the structure. And automatically create event-based and state-based tables (DDL)
for all data sources to allow data scientists to access the data via their lingua franca, SQL, within minutes.
Hadoop’s capabilities offer untapped potential for business insights but companies often get weighed down with DIY platforms and fail to keep up with the requirements. Join this Dell EMC session which will address this challenge with ready bundles to quickly deliver solutions for ETL offload, Single View, & IoT.
Get more value from your big data:
• Deploy big data applications faster
• Increase business agility
• Confidently deliver high performance and endless scale
• Improve IT operational efficiency
Speaker
Shawn Smith, Big Data Specialist, Dell EMC
Data Driving Yahoo Mail Growth and Evolution with a 50 PB Hadoop WarehouseDataWorks Summit
Yahoo Mail has 200+ million users a month and generates hundreds of terabytes of data per day, which continues to grow steadily. The nature of email messages has also evolved: for example, today the majority of them are generated by machines, consisting of newsletters, social media notifications, purchase invoices, travel bookings, and the like, which drove innovations in product development to help users organize their inboxes.
Since 2014, the Yahoo Mail Data Engineering team took on the task of revamping the Mail data warehouse and analytics infrastructure in order to drive the continued growth and evolution of Yahoo Mail. Along the way we have built a 50 PB Hadoop warehouse, and surrounding analytics and machine learning programs that have transformed the way data plays in Yahoo Mail.
In this session we will share our experience from this 3 year journey, from the system architecture, analytics systems built, to the learnings from development and drive for adoption.
Hadoop clusters can store nearly everything in a cheap and blazingly fast way to your data lake. Answering questions and gaining insights out of this ever growing stream becomes the decisive part for many businesses. Increasingly data has a natural structure as a graph, with vertices linked by edges, and many questions arising about the data involve graph traversals or other complex queries, for which one does not have an a priori given bound on the length of paths.
Spark with GraphX is great for answering relatively simple graph questions which are worth starting a Spark job for, because they essentially involve the whole graph. But does it make sense to start one for every ad-hoc query or is it suitable for complex real-time queries?
In this talk I will introduce an alternative solution that adds those features to an existing Hadoop/Spark setup and enables real-time insights. I will address the following topics:
* Challenges in gaining deeper insights from large amounts of graph data
* Benefits and limitations of graph analysis with Spark
* Introduction to ArangoDB SmartGraphs
* Deployment of Hadoop, Spark and ArangoDB using DC/OS
* Performing complex queries on billions of nodes and vertices leveraging ArangoDB SmartGraphs (Live Demo)
Insights into Real World Data Management ChallengesDataWorks Summit
Data is your most valuable business asset and it's also your biggest challenge. This challenge and opportunity means we continually face significant road blocks toward becoming a data driven organisation. From the management of data, to the bubbling open source frameworks, the limited industry skills to surmounting time and cost pressures, our challenge in data is big.
We all want and need a “fit for purpose” approach to management of data, especially Big Data, and overcoming the ongoing challenges around the ‘3Vs’ means we get to focus on the most important V - ‘Value’.Come along and join the discussion on how Oracle Big Data Cloud provides Value in the management of data and supports your move toward becoming a data driven organisation.
Speaker
Noble Raveendran, Principal Consultant, Oracle
Hadoop makes it relatively easy to store petabytes of data. However, storing data is not enough; columnar layouts for storage and in-memory execution allow the analysis of large amounts of data very quickly and efficiently. It provides the ability for multiple applications to share a common data representation and perform operations at full CPU throughput using SIMD and Vectorization. For interoperability, row based encodings (CSV, Thrift, Avro) combined with general purpose compression algorithms (GZip, LZO, Snappy) are common but inefficient. As discussed extensively in the database literature, a columnar layout with statistics and sorting provides vertical and horizontal partitioning, thus keeping IO to a minimum. Additionally a number of key big data technologies have or will soon have in-memory columnar capabilities. This includes Kudu, Ibis and Drill. Sharing a common in-memory columnar representation allows interoperability without the usual cost of serialization.
Understanding modern CPU architecture is critical to maximizing processing throughput. We’ll discuss the advantages of columnar layouts in Parquet and Arrow for in-memory processing and data encodings used for storage (dictionary, bit-packing, prefix coding). We’ll dissect and explain the design choices that enable us to achieve all three goals of interoperability, space and query efficiency. In addition, we’ll provide an overview of what’s coming in Parquet and Arrow in the next year.
Improving business performance is never easy! The Natixis Pack is like Rugby. Working together is key to scrum success. Our data journey would undoubtedly have been so much more difficult if we had not made the move together.
This session is the story of how ‘The Natixis Pack’ has driven change in its current IT architecture so that legacy systems can leverage some of the many components in Hortonworks Data Platform in order to improve the performance of business applications. During this session, you will hear:
• How and why the business and IT requirements originated
• How we leverage the platform to fulfill security and production requirements
• How we organize a community to:
o Guard all the players, no one gets left on the ground!
o Us the platform appropriately (Not every problem is eligible for Big Data and standard databases are not dead)
• What are the most usable, the most interesting and the most promising technologies in the Apache Hadoop community
We will finish the story of a successful rugby team with insight into the special skills needed from each player to win the match!
DETAILS
This session is part business, part technical. We will talk about infrastructure, security and project management as well as the industrial usage of Hive, HBase, Kafka, and Spark within an industrial Corporate and Investment Bank environment, framed by regulatory constraints.
R is a hugely popular platform for Data Scientists to create analytic models in many different domains. It is simple and ubiquitous. And a large number of readily available packages make it very powerful for statistical computing. But when these applications should move from the science lab to the production environment of large enterprises a new set of challenges arises. Independently of R, Spark has been very successful as a powerful general-purpose computing platform that leverages clusters of computers and is able to process data at a scale that has not been feasible before.
With the introduction of SparkR an exciting new option to productionize Data Science applications has been made available. This talk will give insight into two real-life projects at major enterprises where Data Science applications in R have been migrated to SparkR.
Suggested Topics:
• Dealing with platform challenges: R was not installed on the cluster. We show how to execute SparkR on a Yarn cluster with a dynamic deployment of R.
• Integrating Data Engineering and Data Science: we highlight the technical and cultural challenges that arise from closely integrating these two different areas.
• Separation of concerns: we describe how to disentangle ETL and data preparation from analytic computing and statistical methods.
• Scaling R with SparkR: we present what options SparkR offers to scale R applications and how we applied them to different areas such as time series forecasting and web analytics.
• Performance Improvements: we will show benchmarks for an R applications that took over 20 hours on a single server/single-threaded setup. With moderate effort we have been able to reduce that number to 15 minutes with SparkR. And we will show how we plan to further reduces this to less than a minute in the future.
• Mixing SparkR, SparkSQL and MLlib: we show how we combined the three different libraries to maximize efficiency.
Summary and Outlook: we describe what we have learnt so far, what the biggest gaps currently are and what challenges we expect to solve in the short- to mid-term.
Innovation in the Enterprise Rent-A-Car Data WarehouseDataWorks Summit
Big Data adoption is a journey. Depending on the business the process can take weeks, months, or even years. With any transformative technology the challenges have less to do with the technology and more to do with how a company adapts itself to a new way of thinking about data. Building a Center of Excellence is one way for IT to help drive success.
This talk will explore Enterprise Holdings Inc. (which operates the Enterprise Rent-A-Car, National Car Rental and Alamo Rent A Car) and their experience with Big Data. EHI’s journey started in 2013 with Hadoop as a POC and today are working to create the next generation data warehouse in Microsoft’s Azure cloud utilizing a lambda architecture.
We’ll discuss the Center of Excellence, the roles in the new world, share the things which worked well, and rant about those which didn’t.
No deep Hadoop knowledge is necessary, architect or executive level.
The Unbearable Lightness of Ephemeral ProcessingDataWorks Summit
Ephemeral clusters can be launched quickly (minutes), are pre-configured for a specific processing purpose, and can be brought down quickly as soon as their usefulness has expired. The ability to launch Ephemeral clusters for on-demand processing, quickly and efficiently, is transforming how organizations design, deploy and Manage applications. The velocity and elasticity of fast cluster deployment enables seamless peak-demand provisioning, enables cost optimization by leveraging significantly lower cloud spot pricing, and maximizes utilization of existing compute capacity. Additionally, being able to launch bespoke clusters for specific compute needs in a repeatable fashion and within a shared infrastructure provides flexibility for special purpose processing needs. Organizations can leverage Ephemeral Clusters for parallel compute intensive applications which require short bursts of power but are short lived. In this session we will explore how to design Ephemeral clusters, how to launch, modify and bring them down, as well as application design considerations to maximize Ephemeral clusters usability.
Journey to the Data Lake: How Progressive Paved a Faster, Smoother Path to In...DataWorks Summit
Progressive Insurance is well known for its innovative use of data to better serve its customers, and the important role that Hortonworks Data Platform has played in that transformation. However, as with most things worth doing, the path to the Data Lake was not without its challenges. In this session, I’ll share our top use cases for Hadoop – including telematics and display ads, how a skills shortage turned supporting these applications into a nightmare, and how – and why – we now use Syncsort DMX-h to accelerate enterprise adoption by making it quick and easy (or faster and easier) to populate the data lake – and keep it up to date – with data from across the enterprise. I’ll discuss the different approaches we tried, the benefits of using a tool vs. open source, and how we created our Hadoop Ingestor app using Syncsort DMX-h.
Hadoop has traditionally been an on-premises workload, with very few notable implementations on the cloud. With Organizations either having jumped on the cloud bandwagon or have started planning their expansion into the ecosystem, it is imperative for us to explore how Hadoop conforms to the cloud paradigm. With the coming off age of some very useful cloud paradigms and the nature of Big Data with high seasonality of workloads, this is becoming a very common ask from customers. Robust architectures, elastic scale, open platforms, OSS integrations, and addressing complex pain points will all be part of this lively talk. To be able to implement effective solutions for Big Data in the cloud it is imperative that you understand the core principles and grasp the design principles of how the cloud can enhance the benefits of parallelized analytics. Join this session to understand the nitty-gritties of implementing Big Data in the cloud and the various options therein. Big Data + Cloud is definitely a deadly combination.
Apache Apex brings you the power to quickly build and run big data batch and stream processing applications. But what about visualizing your data in real time as it flows through the Apache Apex applications? Together, we will review Apache Apex, and how it integrates with Apache Hadoop and Apache Kafka to process your big data with streaming computation. Then we will explore the options available to visualize Apex applications metrics and data, including open-source options like REST and PubSub mechanisms in StrAM, as well as features available in the RTS Console like real-time Dashboards and Widgets. We will also look into ways of packaging dashboards inside your Apache Apex applications.
Insights into Real-world Data Management ChallengesDataWorks Summit
Oracle began with the belief that the foundation of IT was managing information. The Oracle Cloud Platform for Big Data is a natural extension of our belief in the power of data. Oracle’s Integrated Cloud is one cloud for the entire business, meeting everyone’s needs. It’s about Connecting people to information through tools which help you combine and aggregate data from any source.
This session will explore how organizations can transition to the cloud by delivering fully managed and elastic Hadoop and Real-time Streaming cloud services to built robust offerings that provide measurable value to the business. We will explore key data management trends and dive deeper into pain points we are hearing about from our customer base.
Adding structure to your streaming pipelines: moving from Spark streaming to ...DataWorks Summit
How do you go from a strictly typed object-based streaming pipeline with simple operations to a structured streaming pipeline with higher order complex relational operations? This is what the Data Engineering team did at GoPro to scale up the development of streaming pipelines for the rapidly growing number of devices and applications.
When big data frameworks such as Hadoop first came to exist, developers were happy because we could finally process large amounts of data without writing complex multi-threaded code or worse yet writing complicated distributed code. Unfortunately, only very simple operations were available such as map and reduce. Almost immediately, higher level operations were desired similar to relational operations. And so Hive and dozens (hundreds?) of SQL-based big data tools became available for more developer-efficient batch processing of massive amounts of data.
In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world, so that nearly every streaming framework now supports higher level relational operations.
In this talk, we will discuss in a very hands-on manner how the streaming data pipelines for GoPro devices and apps have moved from the original Spark streaming with its simple RDD-based operations in Spark 1.x to Spark's structured streaming with its higher level relational operations in Spark 2.x. We will talk about the differences, advantages, and necessary pain points that must be addressed in order to scale relational-based streaming pipelines for massive IoT streams. We will also talk about moving from “hand built” Hadoop/Spark clusters running in the cloud to using a Spark-based cloud service. DAVID WINTERS, Big Data Architect, GoPro and HAO ZOU, Senior Software Engineer, GoPro
The world’s largest enterprises run their infrastructure on Oracle, DB2 and SQL and their critical business operations on SAP applications. Organisations need this data to be available in real-time to conduct necessary analytics. However, delivering this heterogeneous data at the speed it’s required can be a huge challenge because of the complex underlying data models and structures and legacy manual processes which are prone to errors and delays.
Unlock these silos of data and enable the new advanced analytics platforms by attending this session.
Find out how to:
• To overcome common challenges faced by enterprises trying to access their SAP data
• You can integrate SAP data in real-time with change data capture (CDC) technology
• Organisations are using Attunity Replicate for SAP to stream SAP data in to Kafka
Insights into Real World Data Management ChallengesDataWorks Summit
Data is your most valuable business asset and it's also your biggest challenge. This challenge and opportunity means we continually face significant road blocks toward becoming a data driven organisation. From the management of data, to the bubbling open source frameworks, the limited industry skills to surmounting time and cost pressures, our challenge in data is big.
We all want and need a “fit for purpose” approach to management of data, especially Big Data, and overcoming the ongoing challenges around the ‘3Vs’ means we get to focus on the most important V - ‘Value’.Come along and join the discussion on how Oracle Big Data Cloud provides Value in the management of data and supports your move toward becoming a data driven organisation.
Speaker
Noble Raveendran, Principal Consultant, Oracle
Hadoop makes it relatively easy to store petabytes of data. However, storing data is not enough; columnar layouts for storage and in-memory execution allow the analysis of large amounts of data very quickly and efficiently. It provides the ability for multiple applications to share a common data representation and perform operations at full CPU throughput using SIMD and Vectorization. For interoperability, row based encodings (CSV, Thrift, Avro) combined with general purpose compression algorithms (GZip, LZO, Snappy) are common but inefficient. As discussed extensively in the database literature, a columnar layout with statistics and sorting provides vertical and horizontal partitioning, thus keeping IO to a minimum. Additionally a number of key big data technologies have or will soon have in-memory columnar capabilities. This includes Kudu, Ibis and Drill. Sharing a common in-memory columnar representation allows interoperability without the usual cost of serialization.
Understanding modern CPU architecture is critical to maximizing processing throughput. We’ll discuss the advantages of columnar layouts in Parquet and Arrow for in-memory processing and data encodings used for storage (dictionary, bit-packing, prefix coding). We’ll dissect and explain the design choices that enable us to achieve all three goals of interoperability, space and query efficiency. In addition, we’ll provide an overview of what’s coming in Parquet and Arrow in the next year.
Improving business performance is never easy! The Natixis Pack is like Rugby. Working together is key to scrum success. Our data journey would undoubtedly have been so much more difficult if we had not made the move together.
This session is the story of how ‘The Natixis Pack’ has driven change in its current IT architecture so that legacy systems can leverage some of the many components in Hortonworks Data Platform in order to improve the performance of business applications. During this session, you will hear:
• How and why the business and IT requirements originated
• How we leverage the platform to fulfill security and production requirements
• How we organize a community to:
o Guard all the players, no one gets left on the ground!
o Us the platform appropriately (Not every problem is eligible for Big Data and standard databases are not dead)
• What are the most usable, the most interesting and the most promising technologies in the Apache Hadoop community
We will finish the story of a successful rugby team with insight into the special skills needed from each player to win the match!
DETAILS
This session is part business, part technical. We will talk about infrastructure, security and project management as well as the industrial usage of Hive, HBase, Kafka, and Spark within an industrial Corporate and Investment Bank environment, framed by regulatory constraints.
R is a hugely popular platform for Data Scientists to create analytic models in many different domains. It is simple and ubiquitous. And a large number of readily available packages make it very powerful for statistical computing. But when these applications should move from the science lab to the production environment of large enterprises a new set of challenges arises. Independently of R, Spark has been very successful as a powerful general-purpose computing platform that leverages clusters of computers and is able to process data at a scale that has not been feasible before.
With the introduction of SparkR an exciting new option to productionize Data Science applications has been made available. This talk will give insight into two real-life projects at major enterprises where Data Science applications in R have been migrated to SparkR.
Suggested Topics:
• Dealing with platform challenges: R was not installed on the cluster. We show how to execute SparkR on a Yarn cluster with a dynamic deployment of R.
• Integrating Data Engineering and Data Science: we highlight the technical and cultural challenges that arise from closely integrating these two different areas.
• Separation of concerns: we describe how to disentangle ETL and data preparation from analytic computing and statistical methods.
• Scaling R with SparkR: we present what options SparkR offers to scale R applications and how we applied them to different areas such as time series forecasting and web analytics.
• Performance Improvements: we will show benchmarks for an R applications that took over 20 hours on a single server/single-threaded setup. With moderate effort we have been able to reduce that number to 15 minutes with SparkR. And we will show how we plan to further reduces this to less than a minute in the future.
• Mixing SparkR, SparkSQL and MLlib: we show how we combined the three different libraries to maximize efficiency.
Summary and Outlook: we describe what we have learnt so far, what the biggest gaps currently are and what challenges we expect to solve in the short- to mid-term.
Innovation in the Enterprise Rent-A-Car Data WarehouseDataWorks Summit
Big Data adoption is a journey. Depending on the business the process can take weeks, months, or even years. With any transformative technology the challenges have less to do with the technology and more to do with how a company adapts itself to a new way of thinking about data. Building a Center of Excellence is one way for IT to help drive success.
This talk will explore Enterprise Holdings Inc. (which operates the Enterprise Rent-A-Car, National Car Rental and Alamo Rent A Car) and their experience with Big Data. EHI’s journey started in 2013 with Hadoop as a POC and today are working to create the next generation data warehouse in Microsoft’s Azure cloud utilizing a lambda architecture.
We’ll discuss the Center of Excellence, the roles in the new world, share the things which worked well, and rant about those which didn’t.
No deep Hadoop knowledge is necessary, architect or executive level.
The Unbearable Lightness of Ephemeral ProcessingDataWorks Summit
Ephemeral clusters can be launched quickly (minutes), are pre-configured for a specific processing purpose, and can be brought down quickly as soon as their usefulness has expired. The ability to launch Ephemeral clusters for on-demand processing, quickly and efficiently, is transforming how organizations design, deploy and Manage applications. The velocity and elasticity of fast cluster deployment enables seamless peak-demand provisioning, enables cost optimization by leveraging significantly lower cloud spot pricing, and maximizes utilization of existing compute capacity. Additionally, being able to launch bespoke clusters for specific compute needs in a repeatable fashion and within a shared infrastructure provides flexibility for special purpose processing needs. Organizations can leverage Ephemeral Clusters for parallel compute intensive applications which require short bursts of power but are short lived. In this session we will explore how to design Ephemeral clusters, how to launch, modify and bring them down, as well as application design considerations to maximize Ephemeral clusters usability.
Journey to the Data Lake: How Progressive Paved a Faster, Smoother Path to In...DataWorks Summit
Progressive Insurance is well known for its innovative use of data to better serve its customers, and the important role that Hortonworks Data Platform has played in that transformation. However, as with most things worth doing, the path to the Data Lake was not without its challenges. In this session, I’ll share our top use cases for Hadoop – including telematics and display ads, how a skills shortage turned supporting these applications into a nightmare, and how – and why – we now use Syncsort DMX-h to accelerate enterprise adoption by making it quick and easy (or faster and easier) to populate the data lake – and keep it up to date – with data from across the enterprise. I’ll discuss the different approaches we tried, the benefits of using a tool vs. open source, and how we created our Hadoop Ingestor app using Syncsort DMX-h.
Hadoop has traditionally been an on-premises workload, with very few notable implementations on the cloud. With Organizations either having jumped on the cloud bandwagon or have started planning their expansion into the ecosystem, it is imperative for us to explore how Hadoop conforms to the cloud paradigm. With the coming off age of some very useful cloud paradigms and the nature of Big Data with high seasonality of workloads, this is becoming a very common ask from customers. Robust architectures, elastic scale, open platforms, OSS integrations, and addressing complex pain points will all be part of this lively talk. To be able to implement effective solutions for Big Data in the cloud it is imperative that you understand the core principles and grasp the design principles of how the cloud can enhance the benefits of parallelized analytics. Join this session to understand the nitty-gritties of implementing Big Data in the cloud and the various options therein. Big Data + Cloud is definitely a deadly combination.
Apache Apex brings you the power to quickly build and run big data batch and stream processing applications. But what about visualizing your data in real time as it flows through the Apache Apex applications? Together, we will review Apache Apex, and how it integrates with Apache Hadoop and Apache Kafka to process your big data with streaming computation. Then we will explore the options available to visualize Apex applications metrics and data, including open-source options like REST and PubSub mechanisms in StrAM, as well as features available in the RTS Console like real-time Dashboards and Widgets. We will also look into ways of packaging dashboards inside your Apache Apex applications.
Insights into Real-world Data Management ChallengesDataWorks Summit
Oracle began with the belief that the foundation of IT was managing information. The Oracle Cloud Platform for Big Data is a natural extension of our belief in the power of data. Oracle’s Integrated Cloud is one cloud for the entire business, meeting everyone’s needs. It’s about Connecting people to information through tools which help you combine and aggregate data from any source.
This session will explore how organizations can transition to the cloud by delivering fully managed and elastic Hadoop and Real-time Streaming cloud services to built robust offerings that provide measurable value to the business. We will explore key data management trends and dive deeper into pain points we are hearing about from our customer base.
Adding structure to your streaming pipelines: moving from Spark streaming to ...DataWorks Summit
How do you go from a strictly typed object-based streaming pipeline with simple operations to a structured streaming pipeline with higher order complex relational operations? This is what the Data Engineering team did at GoPro to scale up the development of streaming pipelines for the rapidly growing number of devices and applications.
When big data frameworks such as Hadoop first came to exist, developers were happy because we could finally process large amounts of data without writing complex multi-threaded code or worse yet writing complicated distributed code. Unfortunately, only very simple operations were available such as map and reduce. Almost immediately, higher level operations were desired similar to relational operations. And so Hive and dozens (hundreds?) of SQL-based big data tools became available for more developer-efficient batch processing of massive amounts of data.
In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world, so that nearly every streaming framework now supports higher level relational operations.
In this talk, we will discuss in a very hands-on manner how the streaming data pipelines for GoPro devices and apps have moved from the original Spark streaming with its simple RDD-based operations in Spark 1.x to Spark's structured streaming with its higher level relational operations in Spark 2.x. We will talk about the differences, advantages, and necessary pain points that must be addressed in order to scale relational-based streaming pipelines for massive IoT streams. We will also talk about moving from “hand built” Hadoop/Spark clusters running in the cloud to using a Spark-based cloud service. DAVID WINTERS, Big Data Architect, GoPro and HAO ZOU, Senior Software Engineer, GoPro
The world’s largest enterprises run their infrastructure on Oracle, DB2 and SQL and their critical business operations on SAP applications. Organisations need this data to be available in real-time to conduct necessary analytics. However, delivering this heterogeneous data at the speed it’s required can be a huge challenge because of the complex underlying data models and structures and legacy manual processes which are prone to errors and delays.
Unlock these silos of data and enable the new advanced analytics platforms by attending this session.
Find out how to:
• To overcome common challenges faced by enterprises trying to access their SAP data
• You can integrate SAP data in real-time with change data capture (CDC) technology
• Organisations are using Attunity Replicate for SAP to stream SAP data in to Kafka
Bring Your SAP and Enterprise Data to Hadoop, Kafka, and the CloudDataWorks Summit
The world’s largest enterprises run their infrastructure on Oracle, DB2 and SQL and their critical business operations on SAP applications. Organisations need this data to be available in real-time to conduct necessary analytics. However, delivering this heterogeneous data at the speed it’s required can be a huge challenge because of the complex underlying data models and structures and legacy manual processes which are prone to errors and delays.
Unlock these silos of data and enable the new advanced analytics platforms by attending this session.
Find out how to:
• To overcome common challenges faced by enterprises trying to access their SAP data
• You can integrate SAP data in real-time with change data capture (CDC) technology
• Organisations are using Attunity Replicate for SAP to stream SAP data in to Kafka
Speakers:
John Hol, Regional Director, Attunity
Mike Hollobon, Director Business Development, IBT
SAP Data Hub e SUSE Container as a Service PlatformSUSE Italy
SAP Data Hub è una soluzione di integrazione, orchestrazione governance di dati di qualsiasi tipo, varietà e volume, che utilizza Kubernetes come piattaforma, ed è certificato su SUSE CaaS Platform
In questa sessione SAP e SUSE presentano una panoramica delle principali funzionalità e dei vantaggi dell’integrazione delle due soluzioni. (Nicola Bertini, SAP Italia e SUSE)
New from SAP Sybase Replication Server: We now offer support for
• Real-time transactional replication into SAP HANA for non-SAP applications
• Real-time change data capture (CDC) for SAP Data Services
• Disaster recovery for SAP Business Suite applications running on SAP Sybase Adaptive Server Enterprise (SAP Sybase ASE)
• Plus, great new features for current customers
Learn about these new options and other features for existing SAP Sybase Replication Server customers during our Webcast. We’ll show you how the new version of SAP Sybase Replication Server replicates transactional data for non-SAP applications in real-time directly into SAP HANA – without slowing or disrupting the systems that are running the business – to create a real real-time analytics solution.
And for customers running SAP Business Suite on SAP Sybase ASE, we now have a solution that reduces both planned and unplanned downtime using SAP Sybase Replication Server.
Take advantage of this opportunity to learn how your organization can build a better real-time analytics solution and better protect your SAP Business Suite data.
Oracle Big Data Appliance and Big Data SQL for advanced analyticsjdijcks
Overview presentation showing Oracle Big Data Appliance and Oracle Big Data SQL in combination with why this really matters. Big Data SQL brings you the unique ability to analyze data across the entire spectrum of system, NoSQL, Hadoop and Oracle Database.
Accelerate your journey to SAP S/4HANA with SAP’s Business Technology PlatformSAP Technology
Best run companies are already benefiting from SAP S/4HANA. Move to SAP S/4HANA quickly with confidence. SAP EIM and SAP Cloud Platform capabilities help you migrate data, curate, and govern master data, archive unused data, move custom code. With performance, functional, and security test tools, customers can move to SAP S/4HANA with confidence.
Horses for Courses: Database RoundtableEric Kavanagh
The blessing and curse of today's database market? So many choices! While relational databases still dominate the day-to-day business, a host of alternatives has evolved around very specific use cases: graph, document, NoSQL, hybrid (HTAP), column store, the list goes on. And the database tools market is teeming with activity as well. Register for this special Research Webcast to hear Dr. Robin Bloor share his early findings about the evolving database market. He'll be joined by Steve Sarsfield of HPE Vertica, and Robert Reeves of Datical in a roundtable discussion with Bloor Group CEO Eric Kavanagh. Send any questions to info@insideanalysis.com, or tweet with #DBSurvival.
Apache Hadoop and Spark are best-of-breed technologies for distributed processing and storage of very large data sets: Big Data. Join us as we explain how to integrate Salesforce with off-the-shelf big data tools to build flexible applications. You'll also learn how Force.com is evolving in this area and how Big Objects and Data Pipelines will provide Big Data capability within the platform.
Many Organizations are currently processing various types of data and in different formats. Most often this data will be in free form, As the consumers of this data growing it’s imperative that this free-flowing data needs to adhere to a schema. It will help data consumers to have an expectation of about the type of data they are getting and also they will be able to avoid immediate impact if the upstream source changes its format. Having a uniform schema representation also gives the Data Pipeline a really easy way to integrate and support various systems that use different data formats.
SchemaRegistry is a central repository for storing, evolving schemas. It provides an API & tooling to help developers and users to register a schema and consume that schema without having any impact if the schema changed. Users can tag different schemas and versions, register for notifications of schema changes with versions etc.
In this talk, we will go through the need for a schema registry and schema evolution and showcase the integration with Apache NiFi, Apache Kafka, Apache Storm.
There is increasing need for large-scale recommendation systems. Typical solutions rely on periodically retrained batch algorithms, but for massive amounts of data, training a new model could take hours. This is a problem when the model needs to be more up-to-date. For example, when recommending TV programs while they are being transmitted the model should take into consideration users who watch a program at that time.
The promise of online recommendation systems is fast adaptation to changes, but methods of online machine learning from streams is commonly believed to be more restricted and hence less accurate than batch trained models. Combining batch and online learning could lead to a quickly adapting recommendation system with increased accuracy. However, designing a scalable data system for uniting batch and online recommendation algorithms is a challenging task. In this talk we present our experiences in creating such a recommendation engine with Apache Flink and Apache Spark.
DeepLearning is not just a hype - it outperforms state-of-the-art ML algorithms. One by one. In this talk we will show how DeepLearning can be used for detecting anomalies on IoT sensor data streams at high speed using DeepLearning4J on top of different BigData engines like ApacheSpark and ApacheFlink. Key in this talk is the absence of any large training corpus since we are using unsupervised machine learning - a domain current DL research threats step-motherly. As we can see in this demo LSTM networks can learn very complex system behavior - in this case data coming from a physical model simulating bearing vibration data. Once draw back of DeepLearning is that normally a very large labaled training data set is required. This is particularly interesting since we can show how unsupervised machine learning can be used in conjunction with DeepLearning - no labeled data set is necessary. We are able to detect anomalies and predict braking bearings with 10 fold confidence. All examples and all code will be made publicly available and open sources. Only open source components are used.
QE automation for large systems is a great step forward in increasing system reliability. In the big-data world, multiple components have to come together to provide end-users with business outcomes. This means, that QE Automations scenarios need to be detailed around actual use cases, cross-cutting components. The system tests potentially generate large amounts of data on a recurring basis, verifying which is a tedious job. Given the multiple levels of indirection, the false positives of actual defects are higher, and are generally wasteful.
At Hortonworks, we’ve designed and implemented Automated Log Analysis System - Mool, using Statistical Data Science and ML. Currently the work in progress has a batch data pipeline with a following ensemble ML pipeline which feeds into the recommendation engine. The system identifies the root cause of test failures, by correlating the failing test cases, with current and historical error records, to identify root cause of errors across multiple components. The system works in unsupervised mode with no perfect model/stable builds/source-code version to refer to. In addition the system provides limited recommendations to file/open past tickets and compares run-profiles with past runs.
HBase hast established itself as the backend for many operational and interactive use-cases, powering well-known services that support millions of users and thousands of concurrent requests. In terms of features HBase has come a long way, overing advanced options such as multi-level caching on- and off-heap, pluggable request handling, fast recovery options such as region replicas, table snapshots for data governance, tuneable write-ahead logging and so on. This talk is based on the research for the an upcoming second release of the speakers HBase book, correlated with the practical experience in medium to large HBase projects around the world. You will learn how to plan for HBase, starting with the selection of the matching use-cases, to determining the number of servers needed, leading into performance tuning options. There is no reason to be afraid of using HBase, but knowing its basic premises and technical choices will make using it much more successful. You will also learn about many of the new features of HBase up to version 1.3, and where they are applicable.
There has been an explosion of data digitising our physical world – from cameras, environmental sensors and embedded devices, right down to the phones in our pockets. Which means that, now, companies have new ways to transform their businesses – both operationally, and through their products and services – by leveraging this data and applying fresh analytical techniques to make sense of it. But are they ready? The answer is “no” in most cases.
In this session, we’ll be discussing the challenges facing companies trying to embrace the Analytics of Things, and how Teradata has helped customers work through and turn those challenges to their advantage.
In this talk, we will present a new distribution of Hadoop, Hops, that can scale the Hadoop Filesystem (HDFS) by 16X, from 70K ops/s to 1.2 million ops/s on Spotiy's industrial Hadoop workload. Hops is an open-source distribution of Apache Hadoop that supports distributed metadata for HSFS (HopsFS) and the ResourceManager in Apache YARN. HopsFS is the first production-grade distributed hierarchical filesystem to store its metadata normalized in an in-memory, shared nothing database. For YARN, we will discuss optimizations that enable 2X throughput increases for the Capacity scheduler, enabling scalability to clusters with >20K nodes. We will discuss the journey of how we reached this milestone, discussing some of the challenges involved in efficiently and safely mapping hierarchical filesystem metadata state and operations onto a shared-nothing, in-memory database. We will also discuss the key database features needed for extreme scaling, such as multi-partition transactions, partition-pruned index scans, distribution-aware transactions, and the streaming changelog API. Hops (www.hops.io) is Apache-licensed open-source and supports a pluggable database backend for distributed metadata, although it currently only support MySQL Cluster as a backend. Hops opens up the potential for new directions for Hadoop when metadata is available for tinkering in a mature relational database.
In high-risk manufacturing industries, regulatory bodies stipulate continuous monitoring and documentation of critical product attributes and process parameters. On the other hand, sensor data coming from production processes can be used to gain deeper insights into optimization potentials. By establishing a central production data lake based on Hadoop and using Talend Data Fabric as a basis for a unified architecture, the German pharmaceutical company HERMES Arzneimittel was able to cater to compliance requirements as well as unlock new business opportunities, enabling use cases like predictive maintenance, predictive quality assurance or open world analytics. Learn how the Talend Data Fabric enabled HERMES Arzneimittel to become data-driven and transform Big Data projects from challenging, hard to maintain hand-coding jobs to repeatable, future-proof integration designs.
Talend Data Fabric combines Talend products into a common set of powerful, easy-to-use tools for any integration style: real-time or batch, big data or master data management, on-premises or in the cloud.
While you could be tempted assuming data is already safe in a single Hadoop cluster, in practice you have to plan for more. Questions like: "What happens if the entire datacenter fails?, or "How do I recover into a consistent state of data, so that applications can continue to run?" are not a all trivial to answer for Hadoop. Did you know that HDFS snapshots are handling open files not as immutable? Or that HBase snapshots are executed asynchronously across servers and therefore cannot guarantee atomicity for cross region updates (which includes tables)? There is no unified and coherent data backup strategy, nor is there tooling available for many of the included components to build such a strategy. The Hadoop distributions largely avoid this topic as most customers are still in the "single use-case" or PoC phase, where data governance as far as backup and disaster recovery (BDR) is concerned are not (yet) important. This talk first is introducing you to the overarching issue and difficulties of backup and data safety, looking at each of the many components in Hadoop, including HDFS, HBase, YARN, Oozie, the management components and so on, to finally show you a viable approach using built-in tools. You will also learn not to take this topic lightheartedly and what is needed to implement and guarantee a continuous operation of Hadoop cluster based solutions.
Hadoop Distributed File System (HDFS) evolves from a MapReduce-centric storage system to a generic, cost-effective storage infrastructure where HDFS stores all data of inside the organizations. The new use case presents a new sets of challenges to the original HDFS architecture. One challenge is to scale the storage management of HDFS - the centralized scheme within NameNode becomes a main bottleneck which limits the total number of files stored. Although a typical large HDFS cluster is able to store several hundred petabytes of data, it is inefficient to handle large amounts of small files under the current architecture.
In this talk, we introduce our new design and in-progress work that re-architects HDFS to attack this limitation. The storage management is enhanced to a distributed scheme. A new concept of storage container is introduced for storing objects. HDFS blocks are stored and managed as objects in the storage containers instead of being tracked only by NameNode. Storage containers are replicated across DataNodes using a newly-developed high-throughput protocol based on the Raft consensus algorithm. Our current prototype shows that under the new architecture the storage management of HDFS scales 10x better, demonstrating that HDFS is capable of storing billions of files.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
3. Utilities rise to the
smart meter
challenge The mass of information from smart
meters is leading utility suppliers to
reconsider how they use their data
• Smart meters generate TBs of data/month
• Regulatory requirement to retain data for 10 years
• Forecasting energy usage
• Benefits of integrating data
• Meter data could help fraud detection, predict
maintenance requirements and eventually lead to
smart grids which respond intelligently to variations
in supply and demand
4. Agriculture takes advantage of
Precision Farming
Top Line Revenue
Growth and Lower
Costs
• Run Reports in Minutes
versus a Day or Two Days
• Improved and Scalable
Architecture Lowering
Costs
• Accurate Weather
Forecasting Leads to an
Increase in Production
Business
Challenge
SAP HANA with SAP Vora
• Migrate DW to the SAP HANA
• Leverage in-DB Machine Learning for predictive analytics
• Hadoop and Vora for low cost storage and compute of
unstructured data
Technical
Enablers
Cost Effectiveness & Improve Product Yield
• Increase in costs and lost revenue due to forecasting
challenges
• Sugar production requires accurate timing
• Managing strategic acquisitions and multiple farms
Benefits Improve Speed and Accuracy of Weather Data
• Leverage IOT data
• OCR parsing of satellite imagery data
• Focus on automation and improvement of forecasting process
• Improve the UX presentation and options
Business Benefits
6. Data Storage
Scalable and unified storage
across data types and sources
Data Compute
Data processing and analysis,
discovery, enhancement, and
governance, making data usable
Data Consumption
Data-driven insight connected to action
Unifying
the Data
Landscape
Integrating across
storage, compute
and consumption
CIO Imperatives & Challenges
Common Lesson
Big Data journey is incomplete
without business transformation
1.
2.
3.
4.
Big Data Journey
53%:
difficulty
integrating with
other enterprise
systems
49% can’t apply external data quickly enough
to enable context-based decision making
59% Only few analysts
with specialized training can
analyze big data
Harvard Business Reivew Analytic Services in Sep 2015
Need lower skill, production support and
performance optimization costs
9. OLAP on
Hadoop for 360º
view of data Creating business scenarios views:
• Data Browser for viewing and exporting data
• SQL Editor for writing and running SQL scripts
• Modeler to visually create data models with
intuitive web interface
10. Time series data
analysis across
Big Data
-30
-20
-10
0
10
Temperature °C
Halifax Waterloo
Trend | Cyclical | Seasonal | Random | Exception
Efficiently analyze time series data in
distributed environments:
• Interactive access to standard time series
analysis functions using the well-known SQL
language
• Efficient compression allowing analysis of more
data using less memory
• Build time series models visually using Vora
Data Modeler
11. Graph engine
to uncover
connected
data
relationships
Native graph processing for:
• Interactive analysis of graphs using graph extension for SQL
• Supports directed and undirected graphs
• Algorithms for pattern matching, shortest path, and connected
components
1:Actor
NAME=‘Brad Pitt’ 4:MOVIE
TITLE=‘Mr. & Mrs. Smith’
YEAR=2005
RATING:6.5
7:DIRECTOR
NAME=‘Doug Liman’
1:Actor
NAME=‘Angelina Jolie’
3:Actor
NAME=‘Shah Rukh Khan’
5:MOVIE
TITLE=‘Kal Ho Naa Ho’
YEAR=2003
RATING=8.1
4:MOVIE
TITLE=‘My Name is Khan’
YEAR=2010
RATING-8.90
Plays in
Plays in
Director
12. Flexible
storage with
document
store
Support for collection of documents with
different structures:
• Interactive analysis of schema-less JSON data using the
well-known SQL language
• Capability to flexibly add or remove fields from any JSON docs
Document #1
Key: Value
Document #2
{Key: Value, Key:
Value}
Document #3
{Key: Value}
Document #4
Key: {Key: Value,
Key: Value|
Collection
Collection
Collection
Document Store
13. Big Data is complex
It gets more complicated as you scale
14. Introducing: SAP Cloud Platform Big Data Services
Fully Managed Big Data Cloud offering for Production Use
Data Centers optimized for Hadoop
Automated Operations Center
Unified Control Plane
Workbench
Business
Analytics
Search &
Discovery
Data
Exploration
Data Science
& Modeling
Custom
Applications
DataTransfer
Portal
ProactiveHelpdesk
SAP Vora
26. How can you get started with SAP Vora?
1. Blog: https://blogs.sap.com/2016/12/19/a-
look-at-the-sap-hana-vora-1.3-new-analysis-
engines/
2. Developer Community:
https://www.sap.com/developer/topics/hana-
vora.html
Download
and Install
Access From
the Cloud
1. Access from the SAP Cloud Appliance Library
https://www.sap.com/developer/topics/hana-
vora.html
2. Enter credentials
3. Get up and running in the cloud
* Free SAP Vora Developer Edition plus infrastructure cost