DareData

JANUARY 30, 2023

For data scientists, these skills are extremely helpful when it comes to manage and build more optimized data transformation processes, helping models achieve better speed and relability when set in production. Examples of relational databases include MySQL or Microsoft SQL Server. Stanford's Relational Databases and SQL.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Rockset

FEBRUARY 2, 2022

Developers choose this database because of its flexible data model and its inherent scalability as a NoSQL database. Yet, analytics is now a vital part of modern data applications. 2 – Use a Data Virtualization Tool The next approach is to use a data virtualization tool.

MongoDB 52

MongoDB NoSQL Data Warehouse BI 52

Knowledge Hut

SEPTEMBER 6, 2023

Over the past decade, the IT world transformed with a data revolution. The rise of big data and NoSQL changed the game. Systems evolved from simple to complex, and we had to split how we find data from where we store it. Back when I studied Computer Science in the early 2000s, databases like MS Access and Oracle ruled.

Database 52

Database Certification MongoDB MySQL 52

Rockset

SEPTEMBER 13, 2022

Further, data is king, and users want to be able to slice and dice aggregated data as needed to find insights. Users don't want to wait for data engineers to provision new indexes or build new ETL chains. They want unfettered access to the freshest data available. DynamoDB is a NoSQL database provided by AWS.

SQL 52

SQL Database Relational Database AWS 52

Ascend.io

SEPTEMBER 14, 2023

Read More: Data Automation Engineer: Skills, Workflow, and Business Impact Python for Data Engineering Versus SQL, Java, and Scala When diving into the domain of data engineering, understanding the strengths and weaknesses of your chosen programming language is essential. Compiled, targeting the JVM.

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

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

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.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93