Confluent

FEBRUARY 6, 2019

Managed model server in the public cloud like Google Cloud Machine Learning Engine: The cloud provider takes over the burden of availability and reliability. The data scientist “just” deploys its trained model, and production engineers can access it. integration) and preprocessing need to run at scale.

Machine Learning 107

Machine Learning Python Kafka Java 107

ProjectPro

DECEMBER 7, 2021

The second step for building etl pipelines is data transformation, which entails converting the raw data into the format required by the end-application. The transformed data is then placed into the destination data warehouse or data lake. It can also be made accessible as an API and distributed to stakeholders.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

ProjectPro

AUGUST 24, 2021

Create a service account on GCP and download Google Cloud SDK(Software developer kit). Then, Python software and all other dependencies are downloaded and connected to the GCP account for other processes. to accumulate data over a given period for better analysis. Upload it to Azure Data lake storage manually.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

AltexSoft

OCTOBER 21, 2022

This enables systems using Kafka to aggregate data from many sources and to make it consistent. Instead of interfering with each other, Kafka consumers create groups and split data among themselves. cloud data warehouses — for example, Snowflake , Google BigQuery, and Amazon Redshift. Large user community.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93