Cloudera

FEBRUARY 9, 2021

A typical approach that we have seen in customers’ environments is that ETL applications pull data with a frequency of minutes and land it into HDFS storage as an extra Hive table partition file. In this way, the analytic applications are able to turn the latest data into instant business insights. Design Detail.

Data Warehouse 72

Data Warehouse Cloud Kafka Cloud Storage 72

Towards Data Science

APRIL 26, 2023

As data practitioners we want (and love) to build applications on top of our data as seamlessly as possible. Whether you work in BI, Data Science or ML all that matters is the final application and how fast you can see it working end-to-end. A lightinign fast analytics app built with our system. Image from the authors.

Engineering 67

Engineering Data Lake AWS BI 67

AltexSoft

OCTOBER 21, 2022

popular SQL and NoSQL database management systems including Oracle, SQL Server, Postgres, MySQL, MongoDB, Cassandra, and more; cloud storage services — Amazon S3, Azure Blob, and Google Cloud Storage; message brokers such as ActiveMQ, IBM MQ, and RabbitMQ; Big Data processing systems like Hadoop ; and. Kafka vs ETL.

Kafka 93

Kafka Hadoop Big Data ETL Tools 93

Knowledge Hut

JUNE 26, 2023

The processed data are uploaded to Google Cloud Storage, where they are then subjected to transformation with the assistance of dbt. The Structured Streaming API offered by Spark makes it possible for data to be processed in real-time in mini-batches, which in turn offers low-latency processing capabilities.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

ProjectPro

MAY 31, 2021

A big data project is a data analysis project that uses machine learning algorithms and different data analytics techniques on a large dataset for several purposes, including predictive modeling and other advanced analytics applications. The project uses Power BI to visualize batch forecasts.

Big Data 52

Big Data Coding Project Hadoop 52

Edureka

JULY 3, 2024

ADF leverages compute services like Azure HDInsight, Spark, Azure Data Lake Analytics, or Machine Learning to process and analyze the data according to defined requirements. Publish: Transformed data is then published either back to on-premises sources like SQL Server or kept in cloud storage.

Pipeline-centric 52

Pipeline-centric Data Lake Database-centric Data Pipeline 52