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Join Gunnar Morling for a ten minute introduction to Flink and FlinkSQL, as you see him build a Flink pipeline to process data from one Kafka topic to another Kafka topic. In this example, he'll be using RedPanda's Kafka API compatible offering to stream data into and from Flink.
Eric Sammer talks to TFIR with his predictions for 2023 and Data.
The complexity of establishing and maintaining stream processing architectures is widely acknowledged. As the costs of real-time data have become less prohibitive, skillsets are increasingly the bottleneck to leveraging the technology. Decodable is seeking to address this bottleneck directly by letting teams establish capabilities that can filter, route, enrich or transform data streams using SQL, and easily build streaming applications.
Flink SQL provides the ability to process real-time data using Structured Query Language, the de facto standard language used to access and process data, and one that is familiar to a very wide range of developers. It provides an optimized implementation that is difficult to achieve with Java, resulting in the clear advantage of being able get to market faster with efficient real-time applications when using Flink SQL.
Join Gunnar Morling for a ten minute introduction to Flink and FlinkSQL, as you see him build a Flink pipeline to process data from one Kafka topic to another Kafka topic. In this example, he'll be using RedPanda's Kafka API compatible offering to stream data into and from Flink.
Eric Sammer talks to TFIR with his predictions for 2023 and Data.
The complexity of establishing and maintaining stream processing architectures is widely acknowledged. As the costs of real-time data have become less prohibitive, skillsets are increasingly the bottleneck to leveraging the technology. Decodable is seeking to address this bottleneck directly by letting teams establish capabilities that can filter, route, enrich or transform data streams using SQL, and easily build streaming applications.
Flink SQL provides the ability to process real-time data using Structured Query Language, the de facto standard language used to access and process data, and one that is familiar to a very wide range of developers. It provides an optimized implementation that is difficult to achieve with Java, resulting in the clear advantage of being able get to market faster with efficient real-time applications when using Flink SQL.
Join us as we explore the features and benefits of leveraging Decodable for your real-time stream processing needs. As a fully managed stream processing service, Decodable provides pre-built connectors to external systems and leverages SQL to provide a familiar development experience so you can be up and running in minutes, not months.
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Join us as we explore the features and benefits of leveraging Decodable for your real-time stream processing needs. As a fully managed stream processing service, Decodable provides pre-built connectors to external systems and leverages SQL to provide a familiar development experience so you can be up and running in minutes, not months.
In this whitepaper, we'll explore what Flink and stream processing are, what differentiates it from traditional batch processing, how it's used to drive modern applications and services, why it matters in the context of today's business needs, and touch on how it works.
In this whitepaper, we'll explore what Flink and stream processing are, what differentiates it from traditional batch processing, how it's used to drive modern applications and services, why it matters in the context of today's business needs, and touch on how it works.
Join Gunnar Morling for a ten minute introduction to Flink and FlinkSQL, as you see him build a Flink pipeline to process data from one Kafka topic to another Kafka topic. In this example, he'll be using RedPanda's Kafka API compatible offering to stream data into and from Flink.
Join Gunnar Morling for a ten minute introduction to Flink and FlinkSQL, as you see him build a Flink pipeline to process data from one Kafka topic to another Kafka topic. In this example, he'll be using RedPanda's Kafka API compatible offering to stream data into and from Flink.
Learn why every business should consider switching to stream processing, and how it's easier and cheaper than ever before with the Decodable platform.
Learn why every business should consider switching to stream processing, and how it's easier and cheaper than ever before with the Decodable platform.
Decodable in 90 Seconds
In this eBook, we'll walk through five common use cases of real-time stream processing, including Customer 360, food delivery tracking, fraud detection, shipping logistics tracking, and website clickstream analytics. Starting with a sample data schema for each scenario, we'll take a look at how quickly and easily SQL statements can be used to create a series of one or more pipelines to clean, transform, enrich, and aggregate raw incoming data into its desired form.
In this eBook, we'll walk through five common use cases of real-time stream processing, including Customer 360, food delivery tracking, fraud detection, shipping logistics tracking, and website clickstream analytics. Starting with a sample data schema for each scenario, we'll take a look at how quickly and easily SQL statements can be used to create a series of one or more pipelines to clean, transform, enrich, and aggregate raw incoming data into its desired form.
TechCrunch coverage of the announcement of Decodable's enterprise capabilities and enhancements.
TechCrunch coverage of the announcement of Decodable's enterprise capabilities and enhancements.
Learn about the 5 most common mistakes deploying Apache Flink, and how you can avoid them from Flink co-creator and PMC member Robert Metzger. Robert will be joined by Decodable CEO and streaming industry veteran Eric Sammer who'll demo some of the most common stream processing patterns using SQL in a form that you can reproduce yourself in minutes.
Learn about the 5 most common mistakes deploying Apache Flink, and how you can avoid them from Flink co-creator and PMC member Robert Metzger. Robert will be joined by Decodable CEO and streaming industry veteran Eric Sammer who'll demo some of the most common stream processing patterns using SQL in a form that you can reproduce yourself in minutes.
Flink SQL provides the ability to process real-time data using Structured Query Language, the de facto standard language used to access and process data, and one that is familiar to a very wide range of developers. It provides an optimized implementation that is difficult to achieve with Java, resulting in the clear advantage of being able get to market faster with efficient real-time applications when using Flink SQL.
Flink SQL provides the ability to process real-time data using Structured Query Language, the de facto standard language used to access and process data, and one that is familiar to a very wide range of developers. It provides an optimized implementation that is difficult to achieve with Java, resulting in the clear advantage of being able get to market faster with efficient real-time applications when using Flink SQL.
Eric Sammer talks to TFIR with his predictions for 2023 and Data.
Eric Sammer talks to TFIR with his predictions for 2023 and Data.
Apache Flink PMC Chair Robert Metzger summarizes the top features shipped in the latest Apache Flink 1.16 release. Official Flink release announcement: https://flink.apache.org/news/2022/10/28/1.16-announcement.html Slides: https://speakerdeck.com/rmetzger/whats-new-in-flink-1-dot-16 Chapters: 0:00 Introduction 0:17 SQL Gateway 1:36 Hive Compatibility 2:15 Changelog State Backend 4:30 Overdraft Buffers and Unaligned Checkpoints 6:55 RocksDB 7:43 Lookup Joins & Async I/O 9:23 Batch Improvements 12:12 Wrap Up
Apache Flink PMC Chair Robert Metzger summarizes the top features shipped in the latest Apache Flink 1.16 release. Official Flink release announcement: https://flink.apache.org/news/2022/10/28/1.16-announcement.html Slides: https://speakerdeck.com/rmetzger/whats-new-in-flink-1-dot-16 Chapters: 0:00 Introduction 0:17 SQL Gateway 1:36 Hive Compatibility 2:15 Changelog State Backend 4:30 Overdraft Buffers and Unaligned Checkpoints 6:55 RocksDB 7:43 Lookup Joins & Async I/O 9:23 Batch Improvements 12:12 Wrap Up
Robert Metzger, Software Engineer at decodable and PMC member of Apache Flink asks the questions. We talk about the machine learning space in general, relevant machine learning projects for Apache Flink and Apache Flink ML itself: What’s the status of the project right now, and what are the plans for the future.
Robert Metzger, Software Engineer at decodable and PMC member of Apache Flink asks the questions. We talk about the machine learning space in general, relevant machine learning projects for Apache Flink and Apache Flink ML itself: What’s the status of the project right now, and what are the plans for the future.
In this demo-heavy webinar you'll learn why streaming ETL is essential for modern businesses, why current batch architectures with monolithic data warehouses are out of date and how Decodable is approaching the challenges traditionally associated with Streaming ETL.
In this demo-heavy webinar you'll learn why streaming ETL is essential for modern businesses, why current batch architectures with monolithic data warehouses are out of date and how Decodable is approaching the challenges traditionally associated with Streaming ETL.
In this interview, we talk about Change Data Capture with Debezium and Flink with experts in this field as our guests: Gunnar Morling, one of the creators of the Debezium project answers any about CDC in general, as well as Debezium-specific questions. For deep insights into the Flink CDC Connectors project, we have Leonard Xu and Jark Wu, two long-term Flink contributors and leads on the Flink CDC project. Robert Metzger, the PMC Chair of the Apache Flink project asks the questions.
In this interview, we talk about Change Data Capture with Debezium and Flink with experts in this field as our guests: Gunnar Morling, one of the creators of the Debezium project answers any about CDC in general, as well as Debezium-specific questions. For deep insights into the Flink CDC Connectors project, we have Leonard Xu and Jark Wu, two long-term Flink contributors and leads on the Flink CDC project. Robert Metzger, the PMC Chair of the Apache Flink project asks the questions.
In this demo-heavy webinar Gunnar Morling and Sharon Xie answer common questions on change stream processing: - What is Change Data Capture (CDC) and why should I care? - What do I gain when I integrate CDC with Apache Flink? After this introduction, they switch to code to show how it works in a demo that includes CDC sources, stream processing and delivery to an Elastic database for searching.
In this demo-heavy webinar Gunnar Morling and Sharon Xie answer common questions on change stream processing: - What is Change Data Capture (CDC) and why should I care? - What do I gain when I integrate CDC with Apache Flink? After this introduction, they switch to code to show how it works in a demo that includes CDC sources, stream processing and delivery to an Elastic database for searching.
In this talk, originally given at Flink Forward 2022, Decodable founding engineer Sharon Xie explores the key challenges and solutions building a managed Apache Flink solution into the Decodable stream processing platform.
In this talk, originally given at Flink Forward 2022, Decodable founding engineer Sharon Xie explores the key challenges and solutions building a managed Apache Flink solution into the Decodable stream processing platform.
Apache Flink PMC chair, Robert Metzger, in conversation with Gyula Fóra and Mátyás Örhidi, the main contributors of the new Flink Kubernetes Operator launched earlier this year. The Flink Kubernetes Operator is an abstraction layer on top of Kubernetes that makes deployment and operation much easier than apply Flink directly to Kubernetes components. In this discussion we cover the motivation for creating a kubernetes operator as part of the Flink project and dig into the details of using it.
Apache Flink PMC chair, Robert Metzger, in conversation with Gyula Fóra and Mátyás Örhidi, the main contributors of the new Flink Kubernetes Operator launched earlier this year. The Flink Kubernetes Operator is an abstraction layer on top of Kubernetes that makes deployment and operation much easier than apply Flink directly to Kubernetes components. In this discussion we cover the motivation for creating a kubernetes operator as part of the Flink project and dig into the details of using it.
Join David Fabritius as he explores the features and benefits of leveraging Decodable for your real-time stream processing needs. Decodable is a stream processing platform providing the simplest method for moving data anywhere with real-time speed, transformed to match the needs of its destination. As a fully managed stream processing service, Decodable provides pre-built connectors to external systems and leverages SQL to provide a familiar development experience so you can be up and running in minutes, not months.
Join David Fabritius as he explores the features and benefits of leveraging Decodable for your real-time stream processing needs. Decodable is a stream processing platform providing the simplest method for moving data anywhere with real-time speed, transformed to match the needs of its destination. As a fully managed stream processing service, Decodable provides pre-built connectors to external systems and leverages SQL to provide a familiar development experience so you can be up and running in minutes, not months.
This quick introductory tutorial walks you through creating your first data flow in Decodable. Create a free account at https://app.decodable.co/ Run this demo for yourself by following the tutorial (includes all SQL in this video): https://docs.decodable.co/docs/web-quickstart-guide
This quick introductory tutorial walks you through creating your first data flow in Decodable. Create a free account at https://app.decodable.co/ Run this demo for yourself by following the tutorial (includes all SQL in this video): https://docs.decodable.co/docs/web-quickstart-guide
In this demo video Charles Harding builds a stream processing pipeline with SQL using Decodable in under 8 minutes, showing the developer experience, SQL editor and preview function for testing the SQL transformation.
In this demo video Charles Harding builds a stream processing pipeline with SQL using Decodable in under 8 minutes, showing the developer experience, SQL editor and preview function for testing the SQL transformation.
The complexity of establishing and maintaining stream processing architectures is widely acknowledged. As the costs of real-time data have become less prohibitive, skillsets are increasingly the bottleneck to leveraging the technology. Decodable is seeking to address this bottleneck directly by letting teams establish capabilities that can filter, route, enrich or transform data streams using SQL, and easily build streaming applications.
The complexity of establishing and maintaining stream processing architectures is widely acknowledged. As the costs of real-time data have become less prohibitive, skillsets are increasingly the bottleneck to leveraging the technology. Decodable is seeking to address this bottleneck directly by letting teams establish capabilities that can filter, route, enrich or transform data streams using SQL, and easily build streaming applications.
Decodable's CEO, Eric Sammer, explores the challenges faced building a real-time streaming data platform and how Decodable has solved them.
Decodable's CEO, Eric Sammer, explores the challenges faced building a real-time streaming data platform and how Decodable has solved them.
Eric Sammer explains what Apache Flink is and why Booking.com, Pinterest, Stripe, Alibaba and Goldman Sachs are just a few of the companies that rely on Flink.
Eric Sammer explains what Apache Flink is and why Booking.com, Pinterest, Stripe, Alibaba and Goldman Sachs are just a few of the companies that rely on Flink.
Eric Sammer joins host Sam Ramji on the Open || Source || Data podcast.
Eric Sammer joins host Sam Ramji on the Open || Source || Data podcast.
Get ready for another amazing year of learning, exchanging ideas and networking with the best technical minds building the future of data.
Get ready for another amazing year of learning, exchanging ideas and networking with the best technical minds building the future of data.
The Real-Time Analytics Summit (#RTASummit) is an annual conference that brings professionals in the data space together to discuss harnessing actionable insights from real-time data. Join us to learn, teach, connect, and have an amazing time with the best community in the user-facing real-time analytics world.
The Real-Time Analytics Summit (#RTASummit) is an annual conference that brings professionals in the data space together to discuss harnessing actionable insights from real-time data. Join us to learn, teach, connect, and have an amazing time with the best community in the user-facing real-time analytics world.
TFIR's Swapnil Bhartiya interviews Decodable CEO Eric Sammer about the company's mission, stream processing platform and the future of real-time data.
TFIR's Swapnil Bhartiya interviews Decodable CEO Eric Sammer about the company's mission, stream processing platform and the future of real-time data.
August 29-30, Austin TX. Current is intended to be the conference for the emerging data streaming ecosystem, a place for developers, architects, and technical executives to come together to engage with their peers. Kafka Summit itself will continue to thrive and grow at the heart of this new event.
August 29-30, Austin TX. Current is intended to be the conference for the emerging data streaming ecosystem, a place for developers, architects, and technical executives to come together to engage with their peers. Kafka Summit itself will continue to thrive and grow at the heart of this new event.
Kafka Summit is the premier event for developers, architects, data engineers, devops professionals, and anyone else who wants to learn about streaming data. It brings the Apache Kafka community together to share best practices, learn how to build next-generation systems, and discuss the future of streaming technologies.
Kafka Summit is the premier event for developers, architects, data engineers, devops professionals, and anyone else who wants to learn about streaming data. It brings the Apache Kafka community together to share best practices, learn how to build next-generation systems, and discuss the future of streaming technologies.
Flink Forward is the conference dedicated to Apache Flink and the stream processing community. November 6-8 2023, Seattle USA
Flink Forward is the conference dedicated to Apache Flink and the stream processing community. November 6-8 2023, Seattle USA
VentureBeat's analysis of the recent Datastax+Decodable partnership to promote streaming among joint customers
VentureBeat's analysis of the recent Datastax+Decodable partnership to promote streaming among joint customers
Venturebeat's coverage of Decodable's Series A funding announcement
Venturebeat's coverage of Decodable's Series A funding announcement
Eric Sammer is founder and CEO of Decodable and joins the show to discuss the potential of stream processing, its role in modern data platforms, and how it’s being used today.
Eric Sammer is founder and CEO of Decodable and joins the show to discuss the potential of stream processing, its role in modern data platforms, and how it’s being used today.
An Overview of How Security Works With Data. Join Hubert Dulay as he demonstrates approaches to protecting data, and other related security practices such as monitoring and observability.
An Overview of How Security Works With Data. Join Hubert Dulay as he demonstrates approaches to protecting data, and other related security practices such as monitoring and observability.
Need to get data from PostgreSQL to Snowflake? Hubert Dulay is back with another demo, this time showing how to take PostgreSQL data into Decodable and load into Snowflake using Snowpipe.
Need to get data from PostgreSQL to Snowflake? Hubert Dulay is back with another demo, this time showing how to take PostgreSQL data into Decodable and load into Snowflake using Snowpipe.
This video tells the story of an important table that is dropped and how it was easily recovered.
This video tells the story of an important table that is dropped and how it was easily recovered.
In his second Snowflake ingestion video, Hubert Dulay shows how to configure Snowpipe to load data from Decodable to Snowflake.
In his second Snowflake ingestion video, Hubert Dulay shows how to configure Snowpipe to load data from Decodable to Snowflake.
In this demo video, we'll be using changes in an S3 bucket to trigger AWS Lambda functions, sending records to decodable for processing. Hubert Dulay is at the controls once more.
In this demo video, we'll be using changes in an S3 bucket to trigger AWS Lambda functions, sending records to decodable for processing. Hubert Dulay is at the controls once more.
Hubert Dulay is back, this time demonstrating how Decodable enables streaming joins between a PostgreSQL table streamed via CDC and a website clickstream delivered in a Kafka topic on Confluent Cloud.
Hubert Dulay is back, this time demonstrating how Decodable enables streaming joins between a PostgreSQL table streamed via CDC and a website clickstream delivered in a Kafka topic on Confluent Cloud.
In this video Hubert Dulay shows how to configure mTLS (mutual TLS authentication) with Kafka, one of the most common questions the team at Decodable get from our customers. He then uses this configuration with Decodable's stream processing platform to securely transform and transfer data with Kafka.
In this video Hubert Dulay shows how to configure mTLS (mutual TLS authentication) with Kafka, one of the most common questions the team at Decodable get from our customers. He then uses this configuration with Decodable's stream processing platform to securely transform and transfer data with Kafka.
Saketh Kurnool demonstrates Decodable's new Imply Polaris connector with a demo drawing COVID data from a REST endpoint into a Decodable pipeline and output to Imply Polaris.
Saketh Kurnool demonstrates Decodable's new Imply Polaris connector with a demo drawing COVID data from a REST endpoint into a Decodable pipeline and output to Imply Polaris.
First in our series of 2-minute tips, Charles Harding demonstrates the time-saving technique of cloning existing resources as the basis for creating new resources.
First in our series of 2-minute tips, Charles Harding demonstrates the time-saving technique of cloning existing resources as the basis for creating new resources.
Saketh Kurnool demonstrates how to set up the new Decodable Imply-Polaris sink connector to transform and send data to the Apache Druid based cloud service.
Saketh Kurnool demonstrates how to set up the new Decodable Imply-Polaris sink connector to transform and send data to the Apache Druid based cloud service.
Building on the previous CDC video, Eric Sammer explains & demos real-time streaming change data capture (CDC) from PostgresSQL, processing in decodable and updating back into PostgreSQL.
Building on the previous CDC video, Eric Sammer explains & demos real-time streaming change data capture (CDC) from PostgresSQL, processing in decodable and updating back into PostgreSQL.
Eric Sammer explains & demos real-time streaming change data capture (CDC), processing and ingest, taking Debezium format change records from PostgresSQL, processing with Decodable pipelines written in SQL and ingesting to various target systems including S3, Delta Lake (Databricks), PostgresSQL and more.
Eric Sammer explains & demos real-time streaming change data capture (CDC), processing and ingest, taking Debezium format change records from PostgresSQL, processing with Decodable pipelines written in SQL and ingesting to various target systems including S3, Delta Lake (Databricks), PostgresSQL and more.
In this demo John McKinnon shows how to use the new MySQL CDC (Change Data Capture) connector to create a stream of data from a static table, process the change stream using Decodable and sending the data to Clickhouse.
In this demo John McKinnon shows how to use the new MySQL CDC (Change Data Capture) connector to create a stream of data from a static table, process the change stream using Decodable and sending the data to Clickhouse.
In this video Hubert Dulay demonstrates how to load, & process cryptocurrency data from Kafka to AWS S3 using Decodable's stream processing service and SQL transformation.
In this video Hubert Dulay demonstrates how to load, & process cryptocurrency data from Kafka to AWS S3 using Decodable's stream processing service and SQL transformation.
In this video you'll see how to sync MySQL CDC. data to Clickhouse using Decodable pipelines and the Clickhouse connector to transport and transform the data.
In this video you'll see how to sync MySQL CDC. data to Clickhouse using Decodable pipelines and the Clickhouse connector to transport and transform the data.
Like most real-time analytical databases, Tinybird performs best when the data is prepared so that it can focus on low-latency queries. Decodable provides the capability to capture, enrich, and transform the real-time streaming data and send it to Tinybird. This demo builds a fully managed streaming solution from end to end from the source to the real-time web application.
Like most real-time analytical databases, Tinybird performs best when the data is prepared so that it can focus on low-latency queries. Decodable provides the capability to capture, enrich, and transform the real-time streaming data and send it to Tinybird. This demo builds a fully managed streaming solution from end to end from the source to the real-time web application.
Rockset is a real-time analytics database that is capable of low latency, high concurrency analytical queries. It’s a fully managed database service that supports all the major cloud providers. It uses a storage engine called RocksDB which is an open source key-value data store. RocksDB is used in many high performance storage systems like MySQL, Apache Kafka and CockroachDB. RocksDB is written entirely in C++, for maximum performance. RocksDB is ideal for fast, low latency storage such as flash drives and high-speed disk drives. In this demo, we will walk you through how to capture IoT data from a MQTT broker. The data will contain metrics from a cell phone. We’ll then transform it and send it to Rockset and visualize it in a real time dashboard.
Rockset is a real-time analytics database that is capable of low latency, high concurrency analytical queries. It’s a fully managed database service that supports all the major cloud providers. It uses a storage engine called RocksDB which is an open source key-value data store. RocksDB is used in many high performance storage systems like MySQL, Apache Kafka and CockroachDB. RocksDB is written entirely in C++, for maximum performance. RocksDB is ideal for fast, low latency storage such as flash drives and high-speed disk drives. In this demo, we will walk you through how to capture IoT data from a MQTT broker. The data will contain metrics from a cell phone. We’ll then transform it and send it to Rockset and visualize it in a real time dashboard.
In the last few years, Delta Lake has emerged as a popular new standard for cloud storage due to its ability to merge traditional data store features with elastic cloud storage. However, for many who've not worked with it in production, Delta Lake remains something of a mystery and so this training session is designed to increase the understanding of what Delta lake is, its underpinnings, and what use cases it is best suited for. After completing this training, you'll have a deeper understanding of Delta Lake and be ready to implement it in your data architecture.
In the last few years, Delta Lake has emerged as a popular new standard for cloud storage due to its ability to merge traditional data store features with elastic cloud storage. However, for many who've not worked with it in production, Delta Lake remains something of a mystery and so this training session is designed to increase the understanding of what Delta lake is, its underpinnings, and what use cases it is best suited for. After completing this training, you'll have a deeper understanding of Delta Lake and be ready to implement it in your data architecture.
Real-time OLAP databases (RTOLAP) or even sometimes called streaming databases, are a special type of databases that are designed to perform OLAP workloads on large data sets In this demo we will cleanse security logs before sending them to Apache Pinot, an RTOLAP, using Decodable.
Real-time OLAP databases (RTOLAP) or even sometimes called streaming databases, are a special type of databases that are designed to perform OLAP workloads on large data sets In this demo we will cleanse security logs before sending them to Apache Pinot, an RTOLAP, using Decodable.
Apache Druid is a real-time analytics database designed for fast OLAP queries on large data sets. Druid powers use cases where real-time and streaming ingestion, fast query performance, and high uptime are important. In this video, we will ingest COVID 19 global statistics data into Apache Druid, cleansing the data so that Druid can easily work with it. Once Druid has the COVID data, we will create a real-time dashboard in Apache SuperSet, an open source business intelligence dashboard. Realtime OLAP databases such as Druid operate faster with pre-processed datasets, reducing their workloads and enabling more focused query execution. Decodable is the ideal solution for performing this pre-processing.
Apache Druid is a real-time analytics database designed for fast OLAP queries on large data sets. Druid powers use cases where real-time and streaming ingestion, fast query performance, and high uptime are important. In this video, we will ingest COVID 19 global statistics data into Apache Druid, cleansing the data so that Druid can easily work with it. Once Druid has the COVID data, we will create a real-time dashboard in Apache SuperSet, an open source business intelligence dashboard. Realtime OLAP databases such as Druid operate faster with pre-processed datasets, reducing their workloads and enabling more focused query execution. Decodable is the ideal solution for performing this pre-processing.
Masking is the process of obscuring information from applications or users that don’t have the permissions to process or view them. Masking data is also a requirement enforced by regulations like Health Insurance Portability and Accountability Act (HIPAA). Personal Identifiable Information (PII) and Protected Health Information (PHI) are examples of data that need to be masked. Real time data masking is easy with Decodable’s built-in functions.
Masking is the process of obscuring information from applications or users that don’t have the permissions to process or view them. Masking data is also a requirement enforced by regulations like Health Insurance Portability and Accountability Act (HIPAA). Personal Identifiable Information (PII) and Protected Health Information (PHI) are examples of data that need to be masked. Real time data masking is easy with Decodable’s built-in functions.
In this video Charles Harding demonstrates the new Decodable Delta Lake connector enabling filtering and reformatting a stream of twitter data (provided by datapm) before analysis in Databricks.
In this video Charles Harding demonstrates the new Decodable Delta Lake connector enabling filtering and reformatting a stream of twitter data (provided by datapm) before analysis in Databricks.
OSQuery is an open source tool that lets you query operating system events using SQL.The events can be fed into a streaming platform, in this case Pulsar, for subsequent transformation and routing on the stream using Decodable.
OSQuery is an open source tool that lets you query operating system events using SQL.The events can be fed into a streaming platform, in this case Pulsar, for subsequent transformation and routing on the stream using Decodable.
In today's incredibly useful demo, Hubert Dulay shows how easy it is to use Decodable to convert XML data into JSON on Kafka topics.
In today's incredibly useful demo, Hubert Dulay shows how easy it is to use Decodable to convert XML data into JSON on Kafka topics.
In event-driven architecture, functions can be used to replace backend microservices. In this blog, we will demonstrate how to use Decodable as a serverless function written in SQL performing stateful transformations, replacing unnecessary microservices in your application. "That microservice could have been a SQL statement" made real, with Decodable. This starts off as an insight on how to achieve more, faster by replacing needless complexity with simplicity, but it really is a great educational piece on change streams, materialized views, CQRS, and stateless VS stateful processing of data.
In event-driven architecture, functions can be used to replace backend microservices. In this blog, we will demonstrate how to use Decodable as a serverless function written in SQL performing stateful transformations, replacing unnecessary microservices in your application. "That microservice could have been a SQL statement" made real, with Decodable. This starts off as an insight on how to achieve more, faster by replacing needless complexity with simplicity, but it really is a great educational piece on change streams, materialized views, CQRS, and stateless VS stateful processing of data.
Flink is an amazing technology; the power to process real-time data on the stream is something that everyone running Kafka, Pulsar, Kinesis and other popular messaging platforms will want to use eventually. But with power comes responsibility, and in the case of Flink, complexity. Decodable's mission is to eliminate this complexity to make stream processing available to everyone. In this video we'll show the relative experience of two Flink-based cloud services; Decodable and Kinesis Data Analytics.
Flink is an amazing technology; the power to process real-time data on the stream is something that everyone running Kafka, Pulsar, Kinesis and other popular messaging platforms will want to use eventually. But with power comes responsibility, and in the case of Flink, complexity. Decodable's mission is to eliminate this complexity to make stream processing available to everyone. In this video we'll show the relative experience of two Flink-based cloud services; Decodable and Kinesis Data Analytics.
In this video, we build a streaming data pipeline with Decodable’s REST APIs using Postman to invoke the APIs from a GUI, and then use Postman to send streaming data into the new pipeline using Decodable's HTTP connector. We also show how to configure Postman with the necessary configuration and variables all within a collection - a set of saved requests that can be loaded and shared with others. Read the accompanying blog here: https://www.decodable.co/blog/using-rest-apis-to-build-pipelines-stream-data-with-postman
In this video, we build a streaming data pipeline with Decodable’s REST APIs using Postman to invoke the APIs from a GUI, and then use Postman to send streaming data into the new pipeline using Decodable's HTTP connector. We also show how to configure Postman with the necessary configuration and variables all within a collection - a set of saved requests that can be loaded and shared with others. Read the accompanying blog here: https://www.decodable.co/blog/using-rest-apis-to-build-pipelines-stream-data-with-postman
Schema-on-write is a feature many engineers have used to their advantage when building data pipelines. In this demo, we will introduce this idea, and show how you can add "guard rails" to your pipelines to ensure they don't break.
Schema-on-write is a feature many engineers have used to their advantage when building data pipelines. In this demo, we will introduce this idea, and show how you can add "guard rails" to your pipelines to ensure they don't break.
Hubert Dulay demonstrates how easy it is to create data products for your data mesh using Decodable with the AsyncAPI open source initiative.
Hubert Dulay demonstrates how easy it is to create data products for your data mesh using Decodable with the AsyncAPI open source initiative.
Josh Mahonin takes us on a whistle-stop tour of the Decodable developer experience including schema version management and update, debugging, pipeline dependency management and data product navigation via schemas in a data mesh setup.
Josh Mahonin takes us on a whistle-stop tour of the Decodable developer experience including schema version management and update, debugging, pipeline dependency management and data product navigation via schemas in a data mesh setup.
Arlo Purcell shows how easy it is to connect to Confluent Cloud as both a sink and source, with auto-detection and import of a complex schema on both ends. In this demo he's transforming Airline industry standard SSIM XML data representing flight schedules.
Arlo Purcell shows how easy it is to connect to Confluent Cloud as both a sink and source, with auto-detection and import of a complex schema on both ends. In this demo he's transforming Airline industry standard SSIM XML data representing flight schedules.
This video shows off the new Decodable Pulsar connector as both a source and sink, followed by the usual Q&A with the team.
This video shows off the new Decodable Pulsar connector as both a source and sink, followed by the usual Q&A with the team.
Learn how to process cryptocurrency exchange rates in real-time using and Decodable's transformations on coinbase transactions fed from DataPM
Learn how to process cryptocurrency exchange rates in real-time using and Decodable's transformations on coinbase transactions fed from DataPM
See how Moonsense's SDK transmits real-time data from a device's accelerometer and touch screen to Decodable for processing before sending to the Moonsense fraud detection model.
See how Moonsense's SDK transmits real-time data from a device's accelerometer and touch screen to Decodable for processing before sending to the Moonsense fraud detection model.
Tim James walks through the Decodable app and then shows the power of stream processing in SQL using AWS S3 and Athena as the destination sink.
Tim James walks through the Decodable app and then shows the power of stream processing in SQL using AWS S3 and Athena as the destination sink.
Eric Sammer and Tim James demonstrate how Decodable connects Kafka to S3 by way of Decodable's own dogfooding - using for internal metrics - in the context of how Decodable connects to a range of systems including Redis, AWS Kinesis, Pulsar, RedPanda, RedShift, Snowflake, Snowpipe, Apache Pinot/StarTree and more.
Eric Sammer and Tim James demonstrate how Decodable connects Kafka to S3 by way of Decodable's own dogfooding - using for internal metrics - in the context of how Decodable connects to a range of systems including Redis, AWS Kinesis, Pulsar, RedPanda, RedShift, Snowflake, Snowpipe, Apache Pinot/StarTree and more.
Decodable CEO Eric Sammer demonstrates the capabilities of the real-time data engineering platform through the new user experience.
Decodable CEO Eric Sammer demonstrates the capabilities of the real-time data engineering platform through the new user experience.
Eric Sammer, CEO of Decodable is joined by founding engineer Sharon Xie as they present the Decodable story & product demos to the Netflix real-time data team. Try Decodable today, start free at http://decodable.co
Eric Sammer, CEO of Decodable is joined by founding engineer Sharon Xie as they present the Decodable story & product demos to the Netflix real-time data team. Try Decodable today, start free at http://decodable.co
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