Rockset

MAY 12, 2022

Lambda systems try to accommodate the needs of both big data-focused data scientists as well as streaming-focused developers by separating data ingestion into two layers. One layer processes batches of historic data. It provides instant views of the real-time data. No need to overprovision in advance.

Analytics Application 52

Analytics Application Lambda Architecture Hadoop Electronics 52

Rockset

MAY 17, 2022

Their query languages, whether SQL-like variants such as CQL (Cassandra) and Druid SQL or wholly custom languages such as MQL (MongoDB), poorly support joins and other complex query commands that are standard to SQL , if they support them at all. This is intentionally not their forte. Learn more at rockset.com.

SQL 52

SQL NoSQL Hadoop MongoDB 52

Rockset

MARCH 15, 2021

For example, instead of denormalizing the data, you could use a query engine that supports joins. This will avoid unnecessary processing during data ingestion and reduce the storage bloat due to redundant data. The Demands of Real-Time Analytics Real-time analytics applications have specific demands (i.e.,

MongoDB 40

MongoDB Data Ingestion Analytics Application Kafka 40

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. Several vendors also offer cloud versions of ClickHouse.

MySQL 52

MySQL Kafka Aggregated Data Architecture 52

ProjectPro

JANUARY 31, 2023

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. When to use MapReduce with Big Data. For example – MongoDB.

Big Data 52

Big Data Hadoop AWS Relational Database 52

Rockset

DECEMBER 9, 2021

Companies that undertook big data projects ran head-long into the high cost, rigidity and complexity of managing complex on-premises data stacks. Lifting-and-shifting their big data environment into the cloud only made things more complex. Every layer in the modern data stack was built for a batch-based world.

Transportation 52

Transportation BI SQL Data Warehouse 52

Rockset

APRIL 15, 2022

So why are their analytics still crawling through in batches instead of real time? It’s probably because their analytics database lacks the features necessary to deliver data-driven decisions accurately in real time. All updates are appended rather than written over existing data records. This has some benefits.

Analytics Application 52

Analytics Application Data Warehouse Raw Data Kafka 52