Knowledge Hut

NOVEMBER 28, 2023

Technical Data Engineer Skills 1.Python Python Python is one of the most looked upon and popular programming languages, using which data engineers can create integrations, data pipelines, integrations, automation, and data cleansing and analysis.

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

Netflix Tech

MARCH 25, 2019

Our data ingestion approach, in a nutshell, is classified broadly into two buckets?—?push In this model, we scan system logs and metadata generated by various compute engines to collect corresponding lineage data. push or pull. Today, we are operating using a pull-heavy model.

Building 87

Building Metadata Transportation Data Ingestion 87

AltexSoft

DECEMBER 23, 2022

It is a data integration process with which you first extract raw information (in its original formats) from various sources and load it straight into a central repository such as a cloud data warehouse , a data lake , or a data lakehouse where you transform it into suitable formats for further analysis and reporting.

Process 52

Process Building Raw Data Data Lake 52

Rockset

SEPTEMBER 6, 2019

This raw data from the devices needs to be enriched with content metadata and geolocation information before it can be processed and analyzed. Our traditional data warehouse solution gives us good results for our reporting analytics, but does not scale very well for real-time analytics.

Metadata 40

Metadata Kafka Data Cleanse SQL 40

ProjectPro

AUGUST 24, 2021

Data Warehousing: Data warehousing utilizes and builds a warehouse for storing data. A data engineer interacts with this warehouse almost on an everyday basis. Data Analytics: A data engineer works with different teams who will leverage that data for business solutions.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

ProjectPro

JANUARY 31, 2023

Why is HDFS only suitable for large data sets and not the correct tool for many small files? NameNode is often given a large space to contain metadata for large-scale files. The metadata should come from a single file for optimal space use and economic benefit. And storing these metadata in RAM will become problematic.

Big Data 52

Big Data Hadoop AWS Relational Database 52

phData: Data Engineering

AUGUST 4, 2023

Snowflake hides user data objects and makes them accessible only through SQL queries through the compute layer. It handles the metadata related to these objects, access control configurations, and query optimization statistics. This includes tasks such as data cleansing, enrichment, and aggregation.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

ProjectPro

OCTOBER 20, 2021

Having multiple data integration routes helps optimize the operational as well as analytical use of data. Experimentation in production Big Data Data Warehouse for core ETL tasks Direct data pipelines Tiered Data Lake 4. 29) What is the difference between MLOps and DevOps?

Machine Learning 52

Machine Learning Algorithm Government Data Science 52

DataKitchen

JUNE 24, 2021

Previously we would have a very laborious data warehouse or data mart initiative and it may take a very long time and have a large price tag. We actually broke down that process and began to understand that the data cleansing and gathering upfront often contributed several months of cycle time to the process.

Pipeline-centric 84

Pipeline-centric Manufacturing Education Data Cleanse 84

phData: Data Engineering

JULY 18, 2023

With the birth of cloud data warehouses, data applications, and generative AI , processing large volumes of data faster and cheaper is more approachable and desired than ever. First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52