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. Such a disaggregated architecture enables ingestion or queries to scale up and down as needed.

Analytics Application 52

Analytics Application Lambda Architecture Hadoop Electronics 52

Rockset

JULY 6, 2022

Similarly, databases are only useful for today’s real-time analytics if they can be both strict and flexible. So are schemaless NoSQL databases, which capably ingest firehoses of data but are poor at extracting complex insights from that data. And the same risk of data errors and data downtime also exists.

NoSQL 52

NoSQL SQL Systems PostgreSQL 52

Rockset

MAY 17, 2022

Limitations of NoSQL SQL supports complex queries because it is a very expressive, mature language. And when systems such as Hadoop and Hive arrived, it married complex queries with big data for the first time. That changed when NoSQL databases such as key-value and document stores came on the scene.

SQL 52

SQL NoSQL Hadoop MongoDB 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. Data Processing: This is the final step in deploying a big data model.

Big Data 52

Big Data Hadoop AWS Relational Database 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