Mlearning.ai

AUGUST 4, 2023

Apache Kafka and R abbitMQ are particularly popular in LEs. In LEs, alongside PostgreSQL , MySQL , Microsoft SQL Server , SQLite , MongoDB , and Redis also enjoy high patronage. Graph 7: Percentage of Programming Languages MiscTech Tools In Both LEs and SMEs: ‘. NET (5+) ’, ‘ pandas ’, ‘ numpy ’, and ‘. NET Framework (1.0–4.8)’

Database 52

Database Apache Kafka SQL AI 52

Analytics Vidhya

JUNE 27, 2020

Overview There are a plethora of data science tools out there – which one should you pick up? Here’s a list of over 20. The post 22 Widely Used Data Science and Machine Learning Tools in 2020 appeared first on Analytics Vidhya.

Machine Learning 400

Machine Learning Machine Learning Data Science Analytics 400

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. Cons Limited connectors.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

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 67

AWS Data Warehouse ML ML 67

AWS Machine Learning Blog

APRIL 19, 2023

Streaming ingestion – An Amazon Kinesis Data Analytics for Apache Flink application backed by Apache Kafka topics in Amazon Managed Streaming for Apache Kafka (MSK) (Amazon MSK) calculates aggregated features from a transaction stream, and an AWS Lambda function updates the online feature store.

ML 77

ML ML Apache Kafka SQL 77

Data Science Dojo

JULY 24, 2023

Spark provides a high-level API in multiple languages like Scala, Python, Java, and SQL, making it accessible to a wide range of developers. Spark offers a more flexible and memory-resilient approach, allowing for iterative data processing and in-memory caching, which significantly improves performance.

Big Data 195

Big Data Big Data Data Engineering Data Engineer 195

DagsHub

APRIL 7, 2024

Thanks to its various operators, it is integrated with Python, Spark, Bash, SQL, and more. 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. This also means that it comes with a large community and comprehensive documentation.

Machine Learning 59

Machine Learning Machine Learning ML ML 59