Rockset

FEBRUARY 2, 2022

Let’s explore five ways to run MongoDB analytics, along with the pros and cons of each method. 1 – Query MongoDB Directly The first and most direct approach is to run your analytical queries directly against MongoDB. 3 – Use a Data Warehouse Next, you can replicate your data to a data warehouse.

MongoDB 52

MongoDB NoSQL Data Warehouse BI 52

Rockset

OCTOBER 4, 2022

Streaming data feeds many real-time analytics applications, from logistics tracking to real-time personalization. Event streams, such as clickstreams, IoT data and other time series data, are common sources of data into these apps. Flink, Kafka and MySQL.

MySQL 52

MySQL Kafka Aggregated Data Architecture 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. Kafka vs ETL.

Kafka 93

Kafka Hadoop Big Data ETL Tools 93

Rockset

APRIL 15, 2022

Mutability is the most important capability, but close behind, and intertwined, is the ability to handle out-of-order data. Out-of-order data are time-stamped events that for a number of reasons arrive after the initial data stream has been ingested by the receiving database or data warehouse.

Analytics Application 52

Analytics Application Data Warehouse Kafka Raw Data 52