IBM Journey to AI blog

JULY 12, 2024

IBM® Event Automation’s event endpoint management capability makes it easy to describe and document your Kafka topics (event sources) according to the open source AsyncAPI Specification. Why is this important? Our AsycnAPI applicability is broadening in our implementation.

Apache Kafka 40

Apache Kafka Clustering 40

IBM Journey to AI blog

NOVEMBER 21, 2023

Apache Kafka is a well-known open-source event store and stream processing platform and has grown to become the de facto standard for data streaming. Apache Kafka transfers data without validating the information in the messages. Event Streams on IBM Cloud provides a Schema Registry as part of its Enterprise plan.

Apache Kafka 119

Apache Kafka Clustering Data Quality Data Governance 119

phData

MAY 3, 2024

How Snowflake Helps Achieve Real-Time Analytics Snowflake is the ideal platform to achieve real-time analytics for several reasons, but two of the biggest are its ability to manage concurrency due to the multi-cluster architecture of Snowflake and its robust connections to 3rd party tools like Kafka. Looking for additional help?

Apache Kafka 52

Apache Kafka Analytics Analytics ETL 52

Data Science Dojo

JULY 24, 2023

Clusters : Clusters are groups of interconnected nodes that work together to process and store data. Clustering allows for improved performance and fault tolerance as tasks can be distributed across nodes. Each node is capable of processing and storing data independently.

Big Data 195

Big Data Big Data Data Engineering Data Engineering 195

IBM Journey to AI blog

NOVEMBER 9, 2023

Apache Kafka is a high-performance, highly scalable event streaming platform. To unlock Kafka’s full potential, you need to carefully consider the design of your application. It’s all too easy to write Kafka applications that perform poorly or eventually hit a scalability brick wall.

Apache Kafka 92

Apache Kafka Algorithm Clustering 92

ODSC - Open Data Science

JULY 22, 2023

Expo Hall ODSC events are more than just data science training and networking events. Thank you to everyone who attended for making this event possible, and showing once again why we do what we do — connecting the greater data science community together to push the industry forward. What’s next?

Apache Kafka 52

Apache Kafka Machine Learning Machine Learning Data Science 52

ODSC - Open Data Science

MAY 24, 2023

Streaming Machine Learning Without a Data Lake The combination of data streaming and ML enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the Apache Kafka ecosystem. Here’s what you can expect from ODSC Europe. Final ODSC Europe 2023 Schedule Released!

Data Lakes 52

Data Lakes ML ML Analytics 52

ODSC - Open Data Science

JULY 14, 2023

The session participants will learn the theory behind compound sparsity, state-of-the-art techniques, and how to apply it in practice using the Neural Magic platform.

Machine Learning 52

Machine Learning Machine Learning Apache Kafka Data Science 52

Mlearning.ai

MAY 16, 2023

In data engineering, the Pub/Sub pattern can be used for various use cases such as real-time data processing, event-driven architectures, and data synchronization across multiple systems. The company can use the Pub/Sub pattern to process customer events such as product views, add to cart, and checkout.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

JULY 30, 2024

Guaranteed Delivery : NiFi ensures that data delivered reliably, even in the event of failures. It maintains a write-ahead log to ensure that the state of FlowFiles preserved, even in the event of a failure. Provenance Repository : This repository records all provenance events related to FlowFiles. Is Apache NiFi Easy to Use?

ETL 52

ETL Data Lakes Big Data Big Data 52

AWS Machine Learning Blog

NOVEMBER 3, 2023

This process comprises two key components: event data and optical tracking data. Event data collection entails gathering the fundamental building blocks of the game. For the precision needed in shot speed calculations, we must ensure that the ball’s position aligns precisely with the moment of the event.

AWS 114

AWS Apache Kafka Data Scientist Data Science 114

Pickl AI

NOVEMBER 4, 2024

Among these tools, Apache Hadoop, Apache Spark, and Apache Kafka stand out for their unique capabilities and widespread usage. Apache Hadoop Hadoop is a powerful framework that enables distributed storage and processing of large data sets across clusters of computers.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

AUGUST 9, 2024

Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. Data Streaming Learning about real-time data collection methods using tools like Apache Kafka and Amazon Kinesis.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

DagsHub

APRIL 7, 2024

Flexibility: Airflow was designed with batch workflows in mind; it was not meant for permanently running event-based workflows. Also, while it is not a streaming solution, we can still use it for such a purpose if combined with systems such as Apache Kafka. Cloud-agnostic and can run on any Kubernetes cluster.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Pickl AI

JULY 20, 2023

Diagnostic Analytics Projects: Diagnostic analytics seeks to determine the reasons behind specific events or patterns observed in the data. 3. Predictive Analytics Projects: Predictive analytics involves using historical data to predict future events or outcomes. Root cause analysis is a typical diagnostic analytics task.

Analytics 40

Analytics Analytics Big Data Big Data 40

DagsHub

OCTOBER 23, 2024

Apache Kafka Apache Kafka is a distributed event streaming platform for real-time data pipelines and stream processing. Kafka is highly scalable and ideal for high-throughput and low-latency data pipeline applications. It allows unstructured data to be moved and processed easily between systems.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

The MLOps Blog

MARCH 15, 2023

Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven. Pricing Up to a million events/month on the free plan. Up to 100 million events/month and a 14-day trial for the starter plan. Server update locks the entire cluster. It connects to many DBs.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

The MLOps Blog

AUGUST 11, 2023

Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., These include shared-nothing architecture, event-driven architecture, and directed acyclic graphs (DAGs). Other areas in ML pipelines: transfer learning, anomaly detection, vector similarity search, clustering, etc. 1 Data Ingestion (e.g.,

ML 52

ML ML Machine Learning Machine Learning 52

IBM Journey to AI blog

AUGUST 22, 2024

Event-driven architecture (EDA) has become more crucial for organizations that want to strengthen their competitive advantage through real-time data processing and responsiveness. In recognizing the benefits of event-driven architectures, many companies have turned to Apache Kafka for their event streaming needs.

Apache Kafka 40

Apache Kafka EDA Clustering 40