Cloudera

FEBRUARY 9, 2021

Today’s customers have a growing need for a faster end to end data ingestion to meet the expected speed of insights and overall business demand. This ‘need for speed’ drives a rethink on building a more modern data warehouse solution, one that balances speed with platform cost management, performance, and reliability.

Data Warehouse 69

Data Warehouse Cloud Kafka Cloud Storage 69

ProjectPro

FEBRUARY 8, 2023

When Glue receives a trigger, it collects the data, transforms it using code that Glue generates automatically, and then loads it into Amazon S3 or Amazon Redshift. Then, Glue writes the job's metadata into the embedded AWS Glue Data Catalog. You can produce code, discover the data schema, and modify it.

AWS 98

AWS Scala Metadata Data Lake 98

Netflix Tech

DECEMBER 14, 2018

A fundamental requirement for any lasting data system is that it should scale along with the growth of the business applications it wishes to serve. NMDB is built to be a highly scalable, multi-tenant, media metadata system that can serve a high volume of write/read throughput as well as support near real-time queries.

Media 94

Media Database Metadata Data Schemas 94

ProjectPro

NOVEMBER 22, 2021

PySpark runs a completely compatible Python instance on the Spark driver (where the task was launched) while maintaining access to the Scala-based Spark cluster access. StructType is a collection of StructField objects that determines column name, column data type, field nullability, and metadata. sports activities).

Hadoop 52

Hadoop Python Datasets Metadata 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. NameNode is often given a large space to contain metadata for large-scale files.

Big Data 52

Big Data Hadoop AWS Relational Database 52

Ascend.io

MAY 19, 2023

In our case, data ingestion, transformation, orchestration, reverse ETL, and observability. This is the modern data stack as we know it today. The modern data stack has become disjointed and complex, slowing data engineering’s productivity and limiting their ability to provide value to the business.

Data Pipeline 52

Data Pipeline Data Engineering Data Engineer Media 52

ProjectPro

AUGUST 21, 2015

Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. Schema Schema on Read Schema on Write Best Fit for Applications Data discovery and Massive Storage/Processing of Unstructured data. are all examples of unstructured data.

Hadoop 40

Hadoop Big Data Metadata Java 40