Knowledge Hut

NOVEMBER 17, 2023

Becoming an Azure Data Engineer in this data-centric landscape is a promising career choice. The main duties of an Azure Data Engineer are planning, developing, deploying, and managing the data pipelines. Master data integration techniques, ETL processes, and data pipeline orchestration using tools like Azure Data Factory.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

Snowflake

JUNE 28, 2023

It provides familiar APIs for various data centric tasks, including data preparation, cleansing, preprocessing, model training, and deployments tasks. In the warehouse model, users can seamlessly run and operationalize data pipelines, ML models, and data applications with user-defined functions (UDFs) and stored procedures (sprocs).

Python 52

Python Accessible Accessibility Pipeline-centric 52

Knowledge Hut

FEBRUARY 27, 2023

Data engineering builds data pipelines for core professionals like data scientists, consumers, and data-centric applications. A data engineer can be a generalist, pipeline-centric, or database-centric. Who is Data Engineer, and What Do They Do?

Data Engineering 52

Data Engineering Data Engineer Database-centric Pipeline-centric 52

AltexSoft

JUNE 7, 2021

It also provides tools for statistics, creating ML pipelines, model evaluation, and more. Written in Scala, the framework also supports Java, Python, and R. As a result, companies can count on a wider pool of talent — if compared with Java-centric Hadoop. Multi-language intuitive APIs. Spark limitations. Pricey hardware.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

Ascend.io

SEPTEMBER 14, 2023

Here’s how Python stacks up against SQL, Java, and Scala based on key factors: Feature Python SQL Java Scala Performance Offers good performance which can be enhanced using libraries like NumPy and Cython. In conclusion, for aspiring or even seasoned data engineers, the depth of Python knowledge required is substantial.

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

AltexSoft

JULY 18, 2023

With its native support for in-memory distributed processing and fault tolerance, Spark empowers users to build complex, multi-stage data pipelines with relative ease and efficiency. It has in-memory computing capabilities to deliver speed, a generalized execution model to support various applications, and Java, Scala, Python, and R APIs.

Big Data 52

Big Data Data Process Process Hadoop 52

Knowledge Hut

SEPTEMBER 26, 2023

This cloud-centric approach ensures scalability, flexibility, and cost-efficiency for your data workloads. Some of the prominent languages supported include: Scala: Ideal for developers who want to leverage the full power of Apache Spark. Python: Widely used for data analysis, scripting, and machine learning.

Data Lake 52

Data Lake Database-centric Machine Learning Pipeline-centric 52

Edureka

JULY 4, 2024

Through an intuitive drag-and-drop interface, users can create sophisticated data pipelines, perform complex transformations, and even implement AI models without writing a single line of code. It supports multiple programming languages including T-SQL, Spark SQL, Python, and Scala. But it doesn’t stop there.

Data Lake 40

Data Lake Pipeline-centric Data Warehouse ETL Tools 40

Databand.ai

DECEMBER 20, 2022

He specializes in distributed systems and data processing at scale, regularly working on data pipelines and taking complex analyses authored by data scientists/analysts and keeping them running in production. He is also a member of The Apache Software Foundation. You can also watch both episodes with Maxime (episodes #18 and #19).

Data Analytics 52

Data Analytics Google Cloud Data Science Data Mining 52