IBM Journey to AI blog

AUGUST 29, 2024

The unique advantages of Apache Flink Apache Flink augments event streaming technologies like Apache Kafka to enable businesses to respond to events more effectively in real time. Integration: Integrates seamlessly with other data systems and platforms, including Apache Kafka, Spark, Hadoop and various databases.

Apache Kafka 45

Apache Kafka Hadoop ETL Data Pipeline 45

AWS Machine Learning Blog

OCTOBER 9, 2024

Using Amazon Redshift ML for anomaly detection Amazon Redshift ML makes it easy to create, train, and apply machine learning models using familiar SQL commands in Amazon Redshift data warehouses. To use this feature, you can write rules or analyzers and then turn on anomaly detection in AWS Glue ETL.

AWS 90

AWS ML ML Data Quality 90

Pickl AI

NOVEMBER 4, 2024

Key components of data warehousing include: ETL Processes: ETL stands for Extract, Transform, Load. ETL is vital for ensuring data quality and integrity. Among these tools, Apache Hadoop, Apache Spark, and Apache Kafka stand out for their unique capabilities and widespread usage.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

AWS Machine Learning Blog

JANUARY 6, 2023

The rules in this engine were predefined and written in SQL, which aside from posing a challenge to manage, also struggled to cope with the proliferation of data from TR’s various integrated data source. Amazon MSK makes it easy to ingest and process streaming data in real time with fully managed Apache Kafka.

AWS 86

AWS Data Warehouse ML ML 86

The MLOps Blog

MARCH 15, 2023

Typical examples include: Airbyte Talend Apache Kafka Apache Beam Apache Nifi While getting control over the process is an ideal position an organization wants to be in, the time and effort needed to build such systems are immense and frequently exceeds the license fee of a commercial offering.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

Pickl AI

JULY 8, 2024

Database Extraction: Retrieval from structured databases using query languages like SQL. Tools such as Python’s Pandas library, Apache Spark, or specialised data cleaning software streamline these processes, ensuring data integrity before further transformation. Aggregation: Summarising data into meaningful metrics or aggregates.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

DagsHub

APRIL 7, 2024

Thanks to its various operators, it is integrated with Python, Spark, Bash, SQL, and more. Flexibility: Its use cases are wider than just machine learning; for example, we can use it to set up ETL pipelines. 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.

Machine Learning 59

Machine Learning Machine Learning ML ML 59