LinkedIn Engineering

DECEMBER 19, 2023

Our RU framework ensures that our big data infrastructure, which consists of over 55,000 hosts and 20 clusters holding exabytes of data, is deployed and updated smoothly by minimizing downtime and avoiding performance degradation. Accessibility of all namenodes. No concurrent upgrades are happening within the cluster.

Big Data 92

Big Data Hadoop Metadata Data 92

Rockset

AUGUST 30, 2021

It eliminates the cost and complexity around data preparation, performance tuning and operations, helping to accelerate the movement from batch to real-time analytics. The latest Rockset release, SQL-based rollups, has made real-time analytics on streaming data a lot more affordable and accessible.

SQL 52

SQL Kafka MongoDB MySQL 52

Ascend.io

SEPTEMBER 14, 2023

We’ll explore its advantages, delve into its applications, and highlight why Python is increasingly becoming the first choice for data engineers worldwide. Why Python for Data Engineering? As the field of data engineering evolves, the need for a versatile, performant, and easily accessible language becomes paramount.

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

ProjectPro

DECEMBER 7, 2021

The second step for building etl pipelines is data transformation, which entails converting the raw data into the format required by the end-application. The transformed data is then placed into the destination data warehouse or data lake. It can also be made accessible as an API and distributed to stakeholders.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 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. The software was subsequently open sourced in 2016.

MySQL 52

MySQL Kafka Aggregated Data Architecture 52

ProjectPro

FEBRUARY 8, 2023

Application programming interfaces (APIs) are used to modify the retrieved data set for integration and to support users in keeping track of all the jobs. Users can schedule ETL jobs, and they can also choose the events that will trigger them. Create schedules or events that will act as job triggers.

AWS 98

AWS Scala Metadata Data Lake 98

ProjectPro

OCTOBER 28, 2015

The aim of selecting an ETL tool is to ensure that data is moving into Hadoop at a frequency that can meet the analytic requirements. Sqoop vs Flume-Comparison of the two Best Data Ingestion Tools Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization What is Sqoop in Hadoop?

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52