IBM Journey to AI blog

JANUARY 8, 2024

In modern enterprises, where operations leave a massive digital footprint, business events allow companies to become more adaptable and able to recognize and respond to opportunities or threats as they occur. Teams want more visibility and access to events so they can reuse and innovate on the work of others.

EDA 92

EDA Apache Kafka Clustering Data Governance 92

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. A schema describes the structure of data. Apache Kafka transfers data without validating the information in the messages. What’s next?

Apache Kafka 116

Apache Kafka Clustering Data Quality Data Governance 116

Data Science Blog

JUNE 27, 2023

In this representation, there is a separate store for events within the speed layer and another store for data loaded during batch processing. The serving layer acts as a mediator, enabling subsequent applications to access the data. On the other hand, the real-time views provide immediate access to the most current data.

Big Data 130

Big Data Big Data Apache Kafka Database 130

Precisely

SEPTEMBER 12, 2023

Precisely data integrity solutions fuel your Confluent and Apache Kafka streaming data pipelines with trusted data that has maximum accuracy, consistency, and context and we’re ready to share more with you at the upcoming Current 2023. Let’s cover some additional information to know before attending.

Data Silos 52

Data Silos Apache Kafka Data Pipeline Data Quality 52

Pickl AI

NOVEMBER 4, 2024

Key components of data warehousing include: ETL Processes: ETL stands for Extract, Transform, Load. This process involves extracting data from multiple sources, transforming it into a consistent format, and loading it into the data warehouse. ETL is vital for ensuring data quality and integrity.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 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 Engineer Data Engineering Data Engineering 52

The MLOps Blog

MARCH 15, 2023

A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59