Tag: Kafka

Author: dirkhamstra. Originally published by The New Stack.As the executive in charge of customer success, I have perhaps the most intimate, on-the-ground understanding of how customers use our product and where they struggle. One request we have received time and again has been a better way to access system- and domain-level metrics and data to help optimize the resources and deployment of a NuoDB implementation. That’s why I’m excited to announce the availability of NuoDB Insights, now available with Linux versions of NuoDB 3.01 and higher. So without further ado, let’s cover the basics. Why NuoDB Insights? NuoDB Insights was ... Read More

Author: . Originally published by The New Stack.As the executive in charge of customer success, I have perhaps the most intimate, on-the-ground understanding of how customers use our product and where they struggle. One request we have received time and again has been a better way to access system- and domain-level metrics and data to help optimize the resources and deployment of a NuoDB implementation. That’s why I’m excited to announce the availability of NuoDB Insights, now available with Linux versions of NuoDB 3.01 and higher. So without further ado, let’s cover the basics. Why NuoDB Insights? NuoDB Insights was ... Read More

Feed: Databasejournal.com - Feature Database Articles. Author: . Microsoft HDInsight is the cloud service that deploys and provisions Hadoop clusters on the Azure cloud.  HDInsight is a completely managed and open source analytics service to support enterprise needs. HDInsight supports a wide variety of scenarios with the help of open source frameworks like Hadoop, Storm, Spark, R Server and Hive, not limited to define a pipeline for E-extract, T-transform, L-load, IoT, machine learning, and many more. With enterprise feedback on non-availability of Kafka as a managed service and guaranteeing Kafka bits are up and running, Microsoft included Kafka as service ... Read More

Feed: SAS Blogs. Author: Thomas Keil. Zur Person: Dr. Volker Stümpflen verantwortet als Leiter Data Strategy & Operations bei der Mediengruppe RTL vor allem den Auf- und Ausbau der Data-Science-Aktivitäten sowie die Realisierung und Monetarisierung speziell analysierter Datenmodelle mit Fokus auf Nutzerverhalten und Nutzerbedürfnisse. Er verfügt über große Erfahrungen mit der Vernetzung großer Datenbanksysteme und Applikationen, basierend auf seiner früheren Tätigkeit als Director Data Science mit Schwerpunkt datengetriebenes Risikomanagement im Finanzanlagebereich. Zu seinen weiteren Stationen gehörte die langjährige Leitung der Gruppe Biologische Informationssysteme am Helmholtz-Zentrum in München mit Schwerpunkt auf semantischen Big-Data-Systemen in der Biomedizin. Zudem war er Gründer und ... Read More

Feed: Big Data Made Simple - One source. Many perspectives.. Author: Baiju NT. For a very long time, women working in the fields of science, technology, engineering and math were unwelcome and underappreciated. Take for example the story of Katherine Johnson and her colleagues, who made remarkable contributions to the early years of NASA’s space program. The world had not even heard of her name until two years ago, when the movie, Hidden Figures, hit the screens. Sadly, it is still a man’s world in the STEM fields, and women struggle every day to find a strong foothold in it ... Read More

Feed: Hazelcast Blog. Author: Can Gencer. We are happy to announce the release of Hazelcast Jet 0.6! This version brings many new improvements which I will try to squeeze into this blog post. Improved streaming support for Pipeline API With 0.5, Jet introduced a new high-level API for Jet, called Pipelines which offered a convenient way to do distributed data processing using a powerful and expressive DSL. With 0.6, this API is now overhauled and extended to support a more complete set of features, including full streaming support with windowing. Previously, stateful streaming operations such as windowing was only possible ... Read More

Feed: Hazelcast Blog. Author: David Brimley. 2018 In-Memory Data Grids resurgent IMDG (In-Memory Data Grids) have been around for well over a decade now and sit in a product sector that slightly overlaps NoSQL functionality. IMDGs initially came to the market in the early nougties when web sessions needed to scale past one sticky web-server, these were the days of what was then known as Tangosol Coherence. Since then the main use case for IMDGs has become general in memory caching and compute, where the IMDG sits between a slower persistent store like an RDBMS/Mainframe (or even a NoSQL Database) ... Read More

Feed: Hortonworks Blog – Hortonworks. Author: Matt Spillar. We’re less than two weeks away from DataWorks Summit Berlin (April 16-19)! We have a number of impressive keynote and breakout speakers lined up. These speakers include Ian Pillay and Bradley Smith from Standard Bank, and Jeroen Wolffensperger and Martijn Groen from Rabobank. Standard Bank Standard Bank South Africa is one of South Africa’s largest financial services groups, operating in 20 countries across Africa and other key markets around the world. It is Africa’s biggest lender by assets, offering a range of banking and related financial services. Ian Pillay and Bradley Smith will be speaking ... Read More

Feed: Hortonworks Blog – Hortonworks. Author: Matt Spillar. At a recent Manufacturing and Transportation Customer Community call we discussed connected products and assets in the field. These calls occur a couple times per quarter and act as an opportunity for leaders in both the manufacturing and transportation industries to have a roundtable discussion regarding use cases, business challenges, and best practices. Industry communities are collaborative, industry focused-communities that are organized and curated by the Hortonworks Industry Solutions Group. The agenda and discussion is driven by the customers. Currently, the Manufacturing Customer Community consists of 104 members across 34 companies, while the ... Read More

Feed: Planet MySQL; Author: Yelp Engineering; Since Yelp introduced its real-time streaming data infrastructure, “Data Pipeline”, it has grown in scope and matured vastly. It now supports some of Yelp’s most critical business requirements in its mission to connect people with great local businesses. Today, it has expanded into a diverse ecosystem of connectors sourcing data from Kafka and MySQL, and sinking data into Cassandra, Elasticsearch, Kafka, MySQL, Redshift, and S3. To ensure that the whole ecosystem is functioning correctly, Yelp’s Data Pipeline infrastructure is continually growing its repertoire of reliability techniques such as write-ahead logging, two-phase commit, fuzz testing, ... Read More