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. The benefit of these tools is that they’re built specifically for data analytics.

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

Rockset

FEBRUARY 25, 2021

Joins are often used in real-time analytics applications to combine streaming data (usually representing events) with static data (like customer information). With Elasticsearch, joins are not a first class citizen and many teams end up denormalizing their data to model relationships.

SQL 40

SQL Data Pipeline Kafka Database 40

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. You can find off-the-shelf links for.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93

Rockset

APRIL 15, 2022

The issue is how the downstream database stores updates and late-arriving data. Traditional transactional databases, such as Oracle or MySQL, were designed with the assumption that data would need to be continuously updated to maintain accuracy. That is called at-least-once semantics.

Analytics Application 52

Analytics Application Data Warehouse Raw Data Kafka 52