http://www.trihedral.com - VTScada includes the best integrated historical management tools in the industry.
The native historical database format can efficiently manage vast amounts of process data without expensive server-level computer hardware. Log any variable or all of them. Easily configure ‘deadbands’ to help reduce database size without losing relevant data.
Predictive Maintenance at the Dutch Railways with Ivo EvertsDatabricks
At the Dutch Railways, we are collecting 10s of billions sensor measurements coming from the train fleet and railroad every year. We use these data to conduct predictive maintenance, such as predicting failure of the train axle bearings and detecting air leakage in the train braking pipes. This is extremely useful, as these failures are notoriously difficult to detect during regular maintenance, while occurring frequently and causing severe delays, damage to material and reputy, and costs.
In this talk, we present how we use the compressor logs in order to detect the occurrence of air leakage in the train braking pipes. Compressor run- and idle- times are extracted from the logs and modelled by a logistic regressor for discriminating between the two classes in normal operational mode. Air leakage will cause the idle times to become shorter as air pressure needs to be leveled more frequently, which can be detected with the logistic model. Then, with a density-based clustering technique, a sequence of such events can be identified, while ignoring outliers due to circumstantial phenomena such as power outage. These clusters are associated to levels of severity, based on which a trend analysis can unlock the expected number of days the compressor will still function before breaking down. This method has been developed by Wan-Jui Lee of the Dutch Railways and published as “Anomaly Detection and Severity Prediction of Air Leakage in Train Braking Pipes” in the International Journal of Prognostics and Health Management in 2017. We have implemented the methods as described in the paper using Python and Spark in a production environment.
A Deep Dive into Stateful Stream Processing in Structured Streaming with Tath...Databricks
Stateful processing is one of the most challenging aspects of distributed, fault-tolerant stream processing. The DataFrame APIs in Structured Streaming make it very easy for the developer to express their stateful logic, either implicitly (streaming aggregations) or explicitly (mapGroupsWithState). However, there are a number of moving parts under the hood which makes all the magic possible. In this talk, I am going to dive deeper into how stateful processing works in Structured Streaming.
In particular, I’m going to discuss the following.
• Different stateful operations in Structured Streaming
• How state data is stored in a distributed, fault-tolerant manner using State Stores
• How you can write custom State Stores for saving state to external storage systems.
Data mining model for the data retrieval from central server configurationijcsit
A server, which is to keep track of heavy document traffic, is unable to filter the documents that are most
relevant and updated for continuous text search queries. This paper focuses on handling continuous text
extraction sustaining high document traffic. The main objective is to retrieve recent updated documents
that are most relevant to the query by applying sliding window technique. Our solution indexes the
streamed documents in the main memory with structure based on the principles of inverted file, and
processes document arrival and expiration events with incremental threshold-based method. It also ensures
elimination of duplicate document retrieval using unsupervised duplicate detection. The documents are
ranked based on user feedback and given higher priority for retrieval.
Big Data is an evolution of Business Intelligence (BI).
Whereas traditional BI relies on data warehouses limited in size
(some terabytes) and it hardly manages unstructured data and
real-time analysis, the era of Big Data opens up a new technological
period offering advanced architectures and infrastructures
allowing sophisticated analyzes taking into account these new
data integrated into the ecosystem of the business . In this article,
we will present the results of an experimental study on the performance
of the best framework of Big Analytics (Spark) with the
most popular databases of NoSQL MongoDB and Hadoop. The
objective of this study is to determine the software combination
that allows sophisticated analysis in real time.
Five Data Models for Sharding | Nordic PGDay 2018 | Craig KerstiensCitus Data
Whether you’re working with a distributed system or an MPP database, a key factor in the flexibility you get with the system is how you shard or partition your data. Do you do it by customer, time, or some random uuid? Here we’ll walk through five different approaches to sharding your data and when you should consider each. If you’re thinking you need to scale beyond a single node this will give you the start of your roadmap for doing so.
Predictive Maintenance at the Dutch Railways with Ivo EvertsDatabricks
At the Dutch Railways, we are collecting 10s of billions sensor measurements coming from the train fleet and railroad every year. We use these data to conduct predictive maintenance, such as predicting failure of the train axle bearings and detecting air leakage in the train braking pipes. This is extremely useful, as these failures are notoriously difficult to detect during regular maintenance, while occurring frequently and causing severe delays, damage to material and reputy, and costs.
In this talk, we present how we use the compressor logs in order to detect the occurrence of air leakage in the train braking pipes. Compressor run- and idle- times are extracted from the logs and modelled by a logistic regressor for discriminating between the two classes in normal operational mode. Air leakage will cause the idle times to become shorter as air pressure needs to be leveled more frequently, which can be detected with the logistic model. Then, with a density-based clustering technique, a sequence of such events can be identified, while ignoring outliers due to circumstantial phenomena such as power outage. These clusters are associated to levels of severity, based on which a trend analysis can unlock the expected number of days the compressor will still function before breaking down. This method has been developed by Wan-Jui Lee of the Dutch Railways and published as “Anomaly Detection and Severity Prediction of Air Leakage in Train Braking Pipes” in the International Journal of Prognostics and Health Management in 2017. We have implemented the methods as described in the paper using Python and Spark in a production environment.
A Deep Dive into Stateful Stream Processing in Structured Streaming with Tath...Databricks
Stateful processing is one of the most challenging aspects of distributed, fault-tolerant stream processing. The DataFrame APIs in Structured Streaming make it very easy for the developer to express their stateful logic, either implicitly (streaming aggregations) or explicitly (mapGroupsWithState). However, there are a number of moving parts under the hood which makes all the magic possible. In this talk, I am going to dive deeper into how stateful processing works in Structured Streaming.
In particular, I’m going to discuss the following.
• Different stateful operations in Structured Streaming
• How state data is stored in a distributed, fault-tolerant manner using State Stores
• How you can write custom State Stores for saving state to external storage systems.
Data mining model for the data retrieval from central server configurationijcsit
A server, which is to keep track of heavy document traffic, is unable to filter the documents that are most
relevant and updated for continuous text search queries. This paper focuses on handling continuous text
extraction sustaining high document traffic. The main objective is to retrieve recent updated documents
that are most relevant to the query by applying sliding window technique. Our solution indexes the
streamed documents in the main memory with structure based on the principles of inverted file, and
processes document arrival and expiration events with incremental threshold-based method. It also ensures
elimination of duplicate document retrieval using unsupervised duplicate detection. The documents are
ranked based on user feedback and given higher priority for retrieval.
Big Data is an evolution of Business Intelligence (BI).
Whereas traditional BI relies on data warehouses limited in size
(some terabytes) and it hardly manages unstructured data and
real-time analysis, the era of Big Data opens up a new technological
period offering advanced architectures and infrastructures
allowing sophisticated analyzes taking into account these new
data integrated into the ecosystem of the business . In this article,
we will present the results of an experimental study on the performance
of the best framework of Big Analytics (Spark) with the
most popular databases of NoSQL MongoDB and Hadoop. The
objective of this study is to determine the software combination
that allows sophisticated analysis in real time.
Five Data Models for Sharding | Nordic PGDay 2018 | Craig KerstiensCitus Data
Whether you’re working with a distributed system or an MPP database, a key factor in the flexibility you get with the system is how you shard or partition your data. Do you do it by customer, time, or some random uuid? Here we’ll walk through five different approaches to sharding your data and when you should consider each. If you’re thinking you need to scale beyond a single node this will give you the start of your roadmap for doing so.
This is a presentation by Peter Coppola, VP of Product and Marketing at Basho Technologies and Matthew Aslett, Research Director at 451 Research. Join them as they discuss whether multi-model databases and polyglot persistence have increased operational complexity. They'll discuss the benefits and importance of NoSQL databases and how the Basho Data Platform helps enterprises leverage Big Data applications.
A whitepaper from qubole about the Tips on how to choose the best SQL Engine for your use case and data workloads
https://www.qubole.com/resources/white-papers/enabling-sql-access-to-data-lakes
Distributing Queries the Citus Way | PostgresConf US 2018 | Marco SlotCitus Data
Citus is a sharding extension for postgres that can efficiently distribute a wide range of SQL queries. It uses postgres' planner hook to transparently intercept and plan queries on "distributed" tables. Citus then executes the queries in parallel across many servers, in a way that delegates most of the heavy lifting back to postgres.
Within Citus, we distinguish between several types of SQL queries, which each have their own planning logic:
Local-only queries
Single-node “router” queries
Multi-node “real-time” queries
Multi-stage queries
Each type of query corresponds to a different use case, and Citus implements several planners and executors using different techniques to accommodate the performance requirements and trade-offs for each use case.
This talk will discuss the internals of the different types of planners and executors for distributing SQL on top of postgres, and how they can be applied to different use cases.
Slides of the Apache Omid presentation at Hadoop Summit 2016 in San Jose, CA. Omid is a flexible, reliable, high performant and scalable transaction manager for HBase.
Analysis and evaluation of riak kv cluster environment using basho benchStevenChike
Many institutions and companies with technological development have been producing large size of structured and unstructured data. Therefore, we need special databases to deal with these data and thus emerged NoSQL databases. They are widely used in the cloud databases and the distributed systems. In the era of big data, those databases provide a scalable high availability solution. So we need new architectures to try to meet the need to store more and more different kinds of different data. In order to arrive at a good structure of large and diverse data, this structure must be tested and analyzed in depth with the use of different benchmark tools. In this paper, we experiment the Riak key-value database to measure their performance in terms of throughput and latency, where huge amounts of data are stored and retrieved in different sizes in a distributed database environment. Throughput and latency of the NoSQL database over different types of experiments and different sizes of data are compared and then results were discussed.
Apache Spark for Library Developers with William Benton and Erik ErlandsonDatabricks
As a developer, data engineer, or data scientist, you’ve seen how Apache Spark is expressive enough to let you solve problems elegantly and efficient enough to let you scale out to handle more data. However, if you’re solving the same problems again and again, you probably want to capture and distribute your solutions so that you can focus on new problems and so other people can reuse and remix them: you want to develop a library that extends Spark.
You faced a learning curve when you first started using Spark, and you’ll face a different learning curve as you start to develop reusable abstractions atop Spark. In this talk, two experienced Spark library developers will give you the background and context you’ll need to turn your code into a library that you can share with the world. We’ll cover: Issues to consider when developing parallel algorithms with Spark, Designing generic, robust functions that operate on data frames and datasets, Extending data frames with user-defined functions (UDFs) and user-defined aggregates (UDAFs), Best practices around caching and broadcasting, and why these are especially important for library developers, Integrating with ML pipelines, Exposing key functionality in both Python and Scala, and How to test, build, and publish your library for the community.
We’ll back up our advice with concrete examples from real packages built atop Spark. You’ll leave this talk informed and inspired to take your Spark proficiency to the next level and develop and publish an awesome library of your own.
Distributed Point-in-Time Recovery with Postgres | PGConf.Russia 2018 | Eren ...Citus Data
Postgres has a nice feature called Point-in-time Recovery (PITR) that would allow you to go back in time. In this talk, we will discuss what are the use-cases of PITR, how to prepare your database for PITR by setting good base backup and WAL shipping setups, with some examples. We will expand the discussion with how to achieve PITR if you have a distributed and sharded Postgres setup by mentioning challenges such as clock differences and ways to overcome them, such as two-phase commit and pg_create_restore_point.
We discuss revise scheduling with streaming files warehouses, which blend the features of traditional files warehouses and also data supply systems. In our setting, external sources push append-only files streams into your warehouse with many inter introduction times. While classic data warehouses are normally refreshed during downtimes, streaming warehouses usually are updated while new files arrive. We design the streaming warehouse revise problem as a scheduling trouble, where jobs correspond to processes which load brand-new data in to tables, and whoever objective is usually to minimize files staleness with time. We next propose the scheduling framework that grips the troubles encountered with a stream manufacturing facility: view hierarchies and also priorities, files consistency, lack of ability to pre-empt changes, heterogeneity connected with update jobs brought on by different inter introduction times and also data quantities among various sources, and also transient clog. A story feature in our framework will be that arranging decisions tend not to depend with properties connected with update jobs such as deadlines, but instead on the effects of revise jobs with data staleness.
Monitoring Postgres at Scale | PostgresConf US 2018 | Lukas FittlCitus Data
Your PostgreSQL database is one of the most important pieces of your architecture - yet the level of introspection available in Postgres is often hard to work with. Its easy to get very detailed information, but what should you really watch out for, send reports on and alert on?
In this talk we'll discuss how query performance statistics can be made accessible to application developers, critical entries one should monitor in the PostgreSQL log files, how to collect EXPLAIN plans at scale, how to watch over autovacuum and VACUUM operations, and how to flag issues based on schema statistics.
We'll also talk a bit about monitoring multi-server setups, first going into high availability and read standbys, logical replication, and then reviewing how monitoring looks like for sharded databases like Citus.
The talk will primarily describe free/open-source tools and statistics views readily available from within Postgres.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
EVALUATING CASSANDRA, MONGO DB LIKE NOSQL DATASETS USING HADOOP STREAMINGijiert bestjournal
An unstructured data poses challenges to storing da ta. Experts estimate that 80 to 90 percent of the d ata in any organization is unstructured. And the amount of uns tructured data in enterprises is growing significan tly� often many times faster than structured databases are gro wing. As structured data is existing in table forma t i,e having proper scheme but unstructured data is schema less database So it�s directly signifying the importance of NoSQL storage Model and Map Reduce platform. For processi ng unstructured data,where in existing it is given to Cassandra dataset. Here in present system along wit h Cassandra dataset,Mongo DB is to be implemented. As Mongo DB provide flexible data model and large amou nt of options for querying unstructured data. Where as Cassandra model their data in such a way as to mini mize the total number of queries through more caref ul planning and renormalizations. It offers basic secondary ind exes but for the best performance it�s recommended to model our data as to use them infrequently. So to process
Containerized Stream Engine to Build Modern Delta LakeDatabricks
As days goes, everything is changing, your business, your analytics platform and your data. So, Deriving the real time insights from this humongous volume of data are key for survival. This robust solution can operate you to the speed of change.
Data Partitioning in Mongo DB with CloudIJAAS Team
Cloud computing offers various and useful services like IAAS, PAAS SAAS for deploying the applications at low cost. Making it available anytime anywhere with the expectation to be it scalable and consistent. One of the technique to improve the scalability is Data partitioning. The alive techniques which are used are not that capable to track the data access pattern. This paper implements the scalable workload-driven technique for polishing the scalability of web applications. The experiments are carried out over cloud using NoSQL data store MongoDB to scale out. This approach offers low response time, high throughput and less number of distributed transaction. The result of partitioning technique is conducted and evaluated using TPC-C benchmark.
Hay Street United Methodist Church to Host 2013 Brunch and MatineeBryce Neier
FAYETTEVILLE, North Carolina (June 2013) – The Hay Street United Methodist Church recently announced the date for its upcoming 2013 Brunch and Matinee, which will be held September 21, 2013. Hay Street is inviting all of its members and supporters to attend, including longtime advocate Bryce Neier, founder and attorney at the Law Offices of Bryce D. Neier. Bryce Neier is an active member of the church and has long supported Hay Street’s Children’s Fund.
This is a presentation by Peter Coppola, VP of Product and Marketing at Basho Technologies and Matthew Aslett, Research Director at 451 Research. Join them as they discuss whether multi-model databases and polyglot persistence have increased operational complexity. They'll discuss the benefits and importance of NoSQL databases and how the Basho Data Platform helps enterprises leverage Big Data applications.
A whitepaper from qubole about the Tips on how to choose the best SQL Engine for your use case and data workloads
https://www.qubole.com/resources/white-papers/enabling-sql-access-to-data-lakes
Distributing Queries the Citus Way | PostgresConf US 2018 | Marco SlotCitus Data
Citus is a sharding extension for postgres that can efficiently distribute a wide range of SQL queries. It uses postgres' planner hook to transparently intercept and plan queries on "distributed" tables. Citus then executes the queries in parallel across many servers, in a way that delegates most of the heavy lifting back to postgres.
Within Citus, we distinguish between several types of SQL queries, which each have their own planning logic:
Local-only queries
Single-node “router” queries
Multi-node “real-time” queries
Multi-stage queries
Each type of query corresponds to a different use case, and Citus implements several planners and executors using different techniques to accommodate the performance requirements and trade-offs for each use case.
This talk will discuss the internals of the different types of planners and executors for distributing SQL on top of postgres, and how they can be applied to different use cases.
Slides of the Apache Omid presentation at Hadoop Summit 2016 in San Jose, CA. Omid is a flexible, reliable, high performant and scalable transaction manager for HBase.
Analysis and evaluation of riak kv cluster environment using basho benchStevenChike
Many institutions and companies with technological development have been producing large size of structured and unstructured data. Therefore, we need special databases to deal with these data and thus emerged NoSQL databases. They are widely used in the cloud databases and the distributed systems. In the era of big data, those databases provide a scalable high availability solution. So we need new architectures to try to meet the need to store more and more different kinds of different data. In order to arrive at a good structure of large and diverse data, this structure must be tested and analyzed in depth with the use of different benchmark tools. In this paper, we experiment the Riak key-value database to measure their performance in terms of throughput and latency, where huge amounts of data are stored and retrieved in different sizes in a distributed database environment. Throughput and latency of the NoSQL database over different types of experiments and different sizes of data are compared and then results were discussed.
Apache Spark for Library Developers with William Benton and Erik ErlandsonDatabricks
As a developer, data engineer, or data scientist, you’ve seen how Apache Spark is expressive enough to let you solve problems elegantly and efficient enough to let you scale out to handle more data. However, if you’re solving the same problems again and again, you probably want to capture and distribute your solutions so that you can focus on new problems and so other people can reuse and remix them: you want to develop a library that extends Spark.
You faced a learning curve when you first started using Spark, and you’ll face a different learning curve as you start to develop reusable abstractions atop Spark. In this talk, two experienced Spark library developers will give you the background and context you’ll need to turn your code into a library that you can share with the world. We’ll cover: Issues to consider when developing parallel algorithms with Spark, Designing generic, robust functions that operate on data frames and datasets, Extending data frames with user-defined functions (UDFs) and user-defined aggregates (UDAFs), Best practices around caching and broadcasting, and why these are especially important for library developers, Integrating with ML pipelines, Exposing key functionality in both Python and Scala, and How to test, build, and publish your library for the community.
We’ll back up our advice with concrete examples from real packages built atop Spark. You’ll leave this talk informed and inspired to take your Spark proficiency to the next level and develop and publish an awesome library of your own.
Distributed Point-in-Time Recovery with Postgres | PGConf.Russia 2018 | Eren ...Citus Data
Postgres has a nice feature called Point-in-time Recovery (PITR) that would allow you to go back in time. In this talk, we will discuss what are the use-cases of PITR, how to prepare your database for PITR by setting good base backup and WAL shipping setups, with some examples. We will expand the discussion with how to achieve PITR if you have a distributed and sharded Postgres setup by mentioning challenges such as clock differences and ways to overcome them, such as two-phase commit and pg_create_restore_point.
We discuss revise scheduling with streaming files warehouses, which blend the features of traditional files warehouses and also data supply systems. In our setting, external sources push append-only files streams into your warehouse with many inter introduction times. While classic data warehouses are normally refreshed during downtimes, streaming warehouses usually are updated while new files arrive. We design the streaming warehouse revise problem as a scheduling trouble, where jobs correspond to processes which load brand-new data in to tables, and whoever objective is usually to minimize files staleness with time. We next propose the scheduling framework that grips the troubles encountered with a stream manufacturing facility: view hierarchies and also priorities, files consistency, lack of ability to pre-empt changes, heterogeneity connected with update jobs brought on by different inter introduction times and also data quantities among various sources, and also transient clog. A story feature in our framework will be that arranging decisions tend not to depend with properties connected with update jobs such as deadlines, but instead on the effects of revise jobs with data staleness.
Monitoring Postgres at Scale | PostgresConf US 2018 | Lukas FittlCitus Data
Your PostgreSQL database is one of the most important pieces of your architecture - yet the level of introspection available in Postgres is often hard to work with. Its easy to get very detailed information, but what should you really watch out for, send reports on and alert on?
In this talk we'll discuss how query performance statistics can be made accessible to application developers, critical entries one should monitor in the PostgreSQL log files, how to collect EXPLAIN plans at scale, how to watch over autovacuum and VACUUM operations, and how to flag issues based on schema statistics.
We'll also talk a bit about monitoring multi-server setups, first going into high availability and read standbys, logical replication, and then reviewing how monitoring looks like for sharded databases like Citus.
The talk will primarily describe free/open-source tools and statistics views readily available from within Postgres.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
EVALUATING CASSANDRA, MONGO DB LIKE NOSQL DATASETS USING HADOOP STREAMINGijiert bestjournal
An unstructured data poses challenges to storing da ta. Experts estimate that 80 to 90 percent of the d ata in any organization is unstructured. And the amount of uns tructured data in enterprises is growing significan tly� often many times faster than structured databases are gro wing. As structured data is existing in table forma t i,e having proper scheme but unstructured data is schema less database So it�s directly signifying the importance of NoSQL storage Model and Map Reduce platform. For processi ng unstructured data,where in existing it is given to Cassandra dataset. Here in present system along wit h Cassandra dataset,Mongo DB is to be implemented. As Mongo DB provide flexible data model and large amou nt of options for querying unstructured data. Where as Cassandra model their data in such a way as to mini mize the total number of queries through more caref ul planning and renormalizations. It offers basic secondary ind exes but for the best performance it�s recommended to model our data as to use them infrequently. So to process
Containerized Stream Engine to Build Modern Delta LakeDatabricks
As days goes, everything is changing, your business, your analytics platform and your data. So, Deriving the real time insights from this humongous volume of data are key for survival. This robust solution can operate you to the speed of change.
Data Partitioning in Mongo DB with CloudIJAAS Team
Cloud computing offers various and useful services like IAAS, PAAS SAAS for deploying the applications at low cost. Making it available anytime anywhere with the expectation to be it scalable and consistent. One of the technique to improve the scalability is Data partitioning. The alive techniques which are used are not that capable to track the data access pattern. This paper implements the scalable workload-driven technique for polishing the scalability of web applications. The experiments are carried out over cloud using NoSQL data store MongoDB to scale out. This approach offers low response time, high throughput and less number of distributed transaction. The result of partitioning technique is conducted and evaluated using TPC-C benchmark.
Hay Street United Methodist Church to Host 2013 Brunch and MatineeBryce Neier
FAYETTEVILLE, North Carolina (June 2013) – The Hay Street United Methodist Church recently announced the date for its upcoming 2013 Brunch and Matinee, which will be held September 21, 2013. Hay Street is inviting all of its members and supporters to attend, including longtime advocate Bryce Neier, founder and attorney at the Law Offices of Bryce D. Neier. Bryce Neier is an active member of the church and has long supported Hay Street’s Children’s Fund.
Our instantly intuitive SCADA software removes frustration from every stage of the HMI / SCADA software lifecycle; from pricing and licensing, to development and support. VTScada is perfect for plant, telemetry, or hosted systems of any size.
Its unique architecture integrates all core SCADA components into one easy-to-use package. Intuitive tools and training options combined with the most reliable support in the industry allow you to confidently start creating fully-featured applications immediately.
29 years of dedication, one outstanding product.
http://www.trihedral.com
Great tips and ideas on effective dog and puppy training. For more information on obedience, grooming, keeping your dog happy and healthy, dog books, dog accessories please visit: http://www.sitstaygofetch.com
Flink Forward SF 2017: Srikanth Satya & Tom Kaitchuck - Pravega: Storage Rei...Flink Forward
Pravega is a stream storage system that we designed and built from the ground up for modern day stream processors such as Flink. Its storage layer is tiered and designed to provide low latency for writing and reading, while being able to store an unbounded amount of stream data that eventually becomes cold. We rely on a high-throughput component to store cold stream data, which is critical to enable applications to rely on Pravega alone for storing stream data. Pravega’s API enables applications to manipulate streams with a set of desirable features such as avoiding duplication and writing data transactionally. Both features are important for applications that require exactly-once semantics. This talk goes into the details of Pravega’s architecture and establishes the need for such a storage system.
In questa sessione verranno analizzate e discusse le problematiche legate alla pubblicazione dei dati da devices in un tipico scenario IoT. Vedremmo come il servizio Event Hub di Microsoft Azure gestisce l'inserimento per pubblicazione e sottoscrizione offrendo una scalabilità flessibile, adattabile a profili di carico variabile e ai picchi provocati dalla connettività intermittente.
Cosmos DB Real-time Advanced Analytics WorkshopDatabricks
The workshop implements an innovative fraud detection solution as a PoC for a bank who provides payment processing services for commerce to their merchant customers all across the globe, helping them save costs by applying machine learning and advanced analytics to detect fraudulent transactions. Since their customers are around the world, the right solutions should minimize any latencies experienced using their service by distributing as much of the solution as possible, as closely as possible, to the regions in which their customers use the service. The workshop designs a data pipeline solution that leverages Cosmos DB for both the scalable ingest of streaming data, and the globally distributed serving of both pre-scored data and machine learning models. Cosmos DB’s major advantage when operating at a global scale is its high concurrency with low latency and predictable results.
This combination is unique to Cosmos DB and ideal for the bank needs. The solution leverages the Cosmos DB change data feed in concert with the Azure Databricks Delta and Spark capabilities to enable a modern data warehouse solution that can be used to create risk reduction solutions for scoring transactions for fraud in an offline, batch approach and in a near real-time, request/response approach. https://github.com/Microsoft/MCW-Cosmos-DB-Real-Time-Advanced-Analytics Takeaway: How to leverage Azure Cosmos DB + Azure Databricks along with Spark ML for building innovative advanced analytics pipelines.
Today’s highly connected world is flooding businesses with big and fast-moving data. The ability to trawl this data ocean and identify actionable insights can deliver a competitive advantage to any organization. The WSO2 Analytics Platform enables businesses to do just that by providing batch, real-time, interactive and predictive analysis capabilities all in one place.
In this tutorial we will
* Plug in the WSO2 Analytics Platform to some common business use cases
* Showcase the numerous capabilities of the platform
* Demonstrate how to collect data, analyze, predict and communicate effectively
* Demonstrate how it can analyze integration, security and IoT scenarios
Stick around till the end and you will walk away with the necessary skills to create a winning data strategy for your organization to stay ahead of its competition.
INOVA GIS Platform represents centralized Enterprise GIS (Geographical Information System) that enables seamless data access for any number of different departments within business organization and beyond. Data can be accessed for viewing, analyzing, editing, etc. Apart from that, data can be presented to wider audience with the possibility to control what type of data and to what extent it will be presented.
Introduction to streaming and messaging flume,kafka,SQS,kinesis Omid Vahdaty
Big data makes you a bit Confused ? messaging? batch processing? data streaming? in flight analytics? Cloud? open source? Flume? kafka? flafka (both)? SQS? kinesis? firehose?
Caching for Microservices Architectures: Session II - Caching PatternsVMware Tanzu
In the first webinar of the series we covered the importance of caching in microservice-based application architectures—in addition to improving performance it also aids in making content available from legacy systems, promotes loose coupling and team autonomy, and provides air gaps that can limit failures from cascading through a system.
To reap these benefits, though, the right caching patterns must be employed. In this webinar, we will examine various caching patterns and shed light on how they deliver the capabilities needed by our microservices. What about rapidly changing data, and concurrent updates to data? What impact do these and other factors have to various use cases and patterns?
Understanding data access patterns, covered in this webinar, will help you make the right decisions for each use case. Beyond the simplest of use cases, caching can be tricky business—join us for this webinar to see how best to use them.
Jagdish Mirani, Cornelia Davis, Michael Stolz, Pulkit Chandra, Pivotal
Maginatics @ SDC 2013: Architecting An Enterprise Storage Platform Using Obje...Maginatics
How did Maginatics build a strongly consistent and secure distributed file system? Niraj Tolia, Chief Architect at Maginatics, gave this presentation on the design of MagFS at the Storage Developer Conference on September 16, 2013.
For more information about MagFS—The File System for the Cloud, visit maginatics.com or contact us directly at info@maginatics.com.
Similar to VTScada 11 Software - Integrated Historian (20)
VTScada removes frustration from every stage of the HMI / SCADA lifecycle. For water & wastewater, power generation, oil & gas, broadcasting, manufacturing, marine systems, airport solutions, food & beverage and many more.
All SCADA Software Should be This Easy.
VTScada™ HMI software is an all-in-one SCADA central for plant and telemetry applications of any size. We designed version 11 to make you productive in your first hour, with instantly intuitive tools and a unique architecture that integrates all core SCADA components in a single, easy-to-use package.
VTScada 11 Software - The Idea Studio - Graphic Development EnvironmentTrihedral
The new VTScada Idea Studio™ is a familiar ribbon-based interface that helps you get started in creating high-impact displays in minutes. Drag-and-drop a wide variety of tag animations, meters, buttons, switches, symbols and images. Draw 3D pipes with just a few clicks. New selection and alignment tools make it easy to keep your displays looking sharp and professional.
http://www.trihedral.com - The VTScada Alarm Notification System (ANS) transmits alarm information anywhere via text-to-voice phone calls, SMS text messages, emails and pagers. Dial into your application using your application security account to check levels, acknowledge alarms, change setpoints, or send commands to equipment.
Our development team created VTScada™ software to work differently from other common monitoring and control products. After 27 years of proven installations around the world, we believe that our approach is fundamentally better for mission critical systems. Below are some of the obvious and not so obvious ways that VTScada stands out.
To maximize up-time and protect your valuable historical data, we developed the most comprehensive and user friendly approach to redundancy in the industry.
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Bob Boule
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UiPath Test Automation using UiPath Test Suite series, part 3
VTScada 11 Software - Integrated Historian
1. Updated April 24, 2015
The VTScada Historian
High reliability. Easy data access.
A Standard Component of VTScada
The VTScada Historian is a fully integrated component of the VTScada software platform,
included at no additional cost and requiring no configuration or database management.
The Historian database is configured automatically to match the number of tags logging data to it. Easy-to-use interfaces
allow for creation of trend displays, tabular data displays and data export. 3rd
party software connectors offer simple
connectivity for external data use by enterprise systems (e.g. reporting, maintenance management, analysis, etc).
A Fast and Compact Database Schema
The historian is pre-configured in every application and has a standardized schema. The integrated Historian database is
file-based, in that data is stored in binary files, which results in very compact storage and fast data read/write access.
Relational databases can be used as an alternative to file-based storage. Microsoft SQL, Oracle and MySQL are optional
databases. They must be purchased separately and are installed and configured as per the respective manufacturers’
instructions. A script is provided with VTScada to automatically configure the schema if using one of these alternatives.
Log Data from Multiple Sources
Log Data Based on Triggers
Log on change with user-configurable deadband (default)
Related event (can also be used for disable/enable)
Log on time/sample period
Operator actions (e.g. Entry of a manual value into a numeric data entry field, change of a setpoint, or a control action)
Data is stored directly to the Historian at the time of trigger occurrence. This eliminates any need for separate real-time
and historical databases.
Simple Historian Status Monitoring
The Historian Status Monitoring Widget provides instant status of write and storage rates. When write rates exceed
storage rates, data is automatically buffered and written in burst mode when the Historian connection is available.
VTScada write rates have been tested to 4,000 values per second for a single tag, thus buffering is usually the result of a
slow network, underpowered CPU, or slow storage media.
2. Updated April 27, 2015
Summary, On-Demand Data
Upon request, the Historian analyzes raw tag data and provides the following time-series summary data, based on a user-
definable duration divided into time slices (e.g. one day’s data divided into 1 hour slices):
Time-weighted average (analog)
Minimum (analog)
Maximum (analog)
Change in value (analog)
Value at start (analog)
Time of Minimum (analog)
Time of Maximum (analog)
Totalizer (analog)
Interpolated (analog)
Diff between start and end (analog)
Zero to non-zero transitions (digital)
Non-zero time (digital)
Database Size, Storage Limiting, and Data Retention Periods
The Historian supports the same number of I/O tags as the licensed SCADA server with which it is integrated. Calculated
values logged to the Historian do not count against the total tag count.
The Historian will automatically grow in total storage size to that available on the available drives. Where additional space
is required, increasing the space available to a logical drive will automatically make that space available to the Historian.
Due to the efficient size of the Historian’s native binary data file format and the low cost of large storage, the Historian is
configured to keep all data by default. However, where storage space is limited, the Historian can be configured to
automatically delete data older than X days or keep a specific number of records for each tag. Data is overwritten based
on a First-In-First-Out (FIFO) methodology.
Multiple Historian Configuration
Any number of Historians can be created for a single application. Different Historians
may be configured to store data for a different period or number of records per tag.
The Historians may use similar or different data storage formats, for example
Historian #1 may use the file-based storage and Historian #2 may use MS SQL Server
or another relational database. Each tag may store data to one Historian.
Redundant Historian Configuration
Since the VTScada Historian is an integrated component of any Runtime or Development Runtime license, any computer
running one of these licenses can be configured as a Historian server (e.g. primary, backup, 2nd
backup, 3rd
backup, etc).
Redundant Historians may also use similar or different data storage formats, for example the Primary Historian may use
the file-based storage and the Backup Historian may use Oracle or another relational database. Each data point will be
stored exactly the same on each redundant Historian. Redundant Historians may be co-located with the Primary Historian
or may be geographically separated as long as an IP connection exists between the two.
#1 VTSCada
#2 SQL Server
#3 Oracle
3. Redundant Historian Data Synchronization
and Data Backfill
All redundant Historians will be identical with regard
to the same schema and a complete, replicated copy
of all data. Timestamps will be matched for each
data-point to the millisecond.
Should the primary database server fail, associated
workstations and Internet clients switch to the next
designated database. When it is restored, historical
data automatically synchronizes across a local or
wide area network at up to 160,000 values per
second. This speed is automatically throttled such
that real-time communications between SCADA
servers is not significantly deteriorated.
Any data on any historian that is missing on another will be propagated automatically regardless of how long it has been
since the databases have communicated.
Long Term Data Storage (eliminates archiving)
In the SCADA industry, archiving is typically adopted for long-term historical data storage due, in part, to a) a lack of online
drive space and b) to ensure data was backed up in the event of Historian failure.
The VTScada Historian eliminates the need for data archiving. VTScada’s efficient binary storage eliminates online drive
space issues allowing the size of the database to scale as required utilizing the drive space available. New space can be
added to the logical drive with the addition of new physical driver, providing unlimited scalability for the Historian.
Establishing redundant Historians is a far more robust backup methodology, allowing any number of redundant distributed
Historians to be updated in real time.
Viewing Historical Data within a VTScada Application
VTScada includes several methods to access
historical data from within an application.
Historical Data Viewer (HDV) Trend View
The HDV provides a continuous view of historical
and real-time data on a single plot timeline.
This standard VTScada interface includes a pen
legend on the bottom and a trend viewing area for
wide graphs of both analog and digital values.
Move the new Marker Line horizontally to see
continuously updated values for each plotted
analog tag at every selected timestamp. New icons
in the Pen Legend allow you to hide individual pens
(tags) or edit their appearance. The HDV provides a continuous view of analog and digital data.