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Learn how to set up a Postgres database on Amazon RDS for change data capture, so that you can emit data change streams to tools like Debezium or managed stream processing platforms such as Decodable.
In this episode of "Data Streaming Quick Tips", Gunnar Morling explains how–and why–to re-key a Kafka topic, and how the Decodable platform can help make that easier.
In this inaugural episode of Data Streaming Quick Tips, Gunnar Morling is taking a look at how to use Flink's Upsert Kafka SQL connector for propagating events from a changelog stream (created via Flink CDC and Debezium) to a topic in a Redpanda cluster.
Learn how to reduce the cost and complexity of streaming data into Snowflake with Decodable with the new Snowpipe Streaming connector.
Learn how to set up a Postgres database on Amazon RDS for change data capture, so that you can emit data change streams to tools like Debezium or managed stream processing platforms such as Decodable.
In this episode of "Data Streaming Quick Tips", Gunnar Morling explains how–and why–to re-key a Kafka topic, and how the Decodable platform can help make that easier.
In this inaugural episode of Data Streaming Quick Tips, Gunnar Morling is taking a look at how to use Flink's Upsert Kafka SQL connector for propagating events from a changelog stream (created via Flink CDC and Debezium) to a topic in a Redpanda cluster.
Learn how to reduce the cost and complexity of streaming data into Snowflake with Decodable with the new Snowpipe Streaming connector.
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.
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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.
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.
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.
Learn how to reduce the cost and complexity of streaming data into Snowflake with Decodable with the new Snowpipe Streaming connector.
Learn how to reduce the cost and complexity of streaming data into Snowflake with Decodable with the new Snowpipe Streaming connector.
In this inaugural episode of Data Streaming Quick Tips, Gunnar Morling is taking a look at how to use Flink's Upsert Kafka SQL connector for propagating events from a changelog stream (created via Flink CDC and Debezium) to a topic in a Redpanda cluster.
In this inaugural episode of Data Streaming Quick Tips, Gunnar Morling is taking a look at how to use Flink's Upsert Kafka SQL connector for propagating events from a changelog stream (created via Flink CDC and Debezium) to a topic in a Redpanda cluster.
In this episode of "Data Streaming Quick Tips", Gunnar Morling explains how–and why–to re-key a Kafka topic, and how the Decodable platform can help make that easier.
In this episode of "Data Streaming Quick Tips", Gunnar Morling explains how–and why–to re-key a Kafka topic, and how the Decodable platform can help make that easier.
In this episode of Data Streaming Quick Tips, you’ll learn how to aggregate the elements of an array with Flink SQL using both the built-in function JSON_ARRAYAGG() as well as a user-defined function (UDF). The results of a one-to-many join are then ingested as a nested document structure into a search index in Elasticsearch.
In this episode of Data Streaming Quick Tips, you’ll learn how to aggregate the elements of an array with Flink SQL using both the built-in function JSON_ARRAYAGG() as well as a user-defined function (UDF). The results of a one-to-many join are then ingested as a nested document structure into a search index in Elasticsearch.
Learn how to set up a Postgres database on Amazon RDS for change data capture, so that you can emit data change streams to tools like Debezium or managed stream processing platforms such as Decodable.
Learn how to set up a Postgres database on Amazon RDS for change data capture, so that you can emit data change streams to tools like Debezium or managed stream processing platforms such as Decodable.
In this episode of TFiR: T3M, Decodable CEO Eric Sammer shares his insights on the current data processing trends and how Decodable’s stream processing platform is helping companies enhance their overall data strategy.
In this episode of TFiR: T3M, Decodable CEO Eric Sammer shares his insights on the current data processing trends and how Decodable’s stream processing platform is helping companies enhance their overall data strategy.
In this episode of the Real-Time Analytics podcast, host Tim Berglund welcomes Eric Sammer, Founder and CEO of Decodable. Eric, an industry leader in event streaming technology, discusses the company's focus on stream processing, real-time data processing, and integration with systems like Apache Pinot and StarTree. The conversation delves into the challenges and complexities of managing data, from data cleansing to structuring for different use cases. They explore the ideal balance between generalized and specialized systems, emphasizing the importance of flexibility. Ultimately, they highlight how stream processing serves as an effective solution to adjust and distribute data intelligently, providing an essential abstraction point.
In this episode of the Real-Time Analytics podcast, host Tim Berglund welcomes Eric Sammer, Founder and CEO of Decodable. Eric, an industry leader in event streaming technology, discusses the company's focus on stream processing, real-time data processing, and integration with systems like Apache Pinot and StarTree. The conversation delves into the challenges and complexities of managing data, from data cleansing to structuring for different use cases. They explore the ideal balance between generalized and specialized systems, emphasizing the importance of flexibility. Ultimately, they highlight how stream processing serves as an effective solution to adjust and distribute data intelligently, providing an essential abstraction point.
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.
When it comes to collecting and accurately utilizing real-time data, companies need a solution that is easy to use, scalable, and most importantly, can reduce costs. Between December 2022 and January 2023, Gatepoint Research invited selected executives to participate in a survey themed Approaches to Real-Time Data Analysis. Management levels represented are all senior decision-makers, with 20 percent holding the title of chief executive or vice president. Half of all respondents say their company could use some help when it comes to making real-time business decisions, acknowledging they are moderately to not at all confident in making decisions such as investing in a price cut, purchasing inventory, or releasing marketing spend.
When it comes to collecting and accurately utilizing real-time data, companies need a solution that is easy to use, scalable, and most importantly, can reduce costs. Between December 2022 and January 2023, Gatepoint Research invited selected executives to participate in a survey themed Approaches to Real-Time Data Analysis. Management levels represented are all senior decision-makers, with 20 percent holding the title of chief executive or vice president. Half of all respondents say their company could use some help when it comes to making real-time business decisions, acknowledging they are moderately to not at all confident in making decisions such as investing in a price cut, purchasing inventory, or releasing marketing spend.
TechCrunch coverage of the announcement of Decodable's enterprise capabilities and enhancements.
TechCrunch coverage of the announcement of Decodable's enterprise capabilities and enhancements.
Doug Atkinson and Decodable CEO Eric Sammer discuss making streaming data engineering easier with Decodable's fully-managed stream processing platform, which allows for the real-time ingestion, integration, and transformation of data to support the development of event-driven applications and services.
Doug Atkinson and Decodable CEO Eric Sammer discuss making streaming data engineering easier with Decodable's fully-managed stream processing platform, which allows for the real-time ingestion, integration, and transformation of data to support the development of event-driven applications and services.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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