Knowledge Hut

APRIL 25, 2023

Data modeling: Data engineers should be able to design and develop data models that help represent complex data structures effectively. Data processing: Data engineers should know data processing frameworks like Apache Spark, Hadoop, or Kafka, which help process and analyze data at scale.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

ProjectPro

DECEMBER 7, 2021

However, you can also pull data from centralized data sources like data warehouses to transform data further and build ETL pipelines for training and evaluating AI agents. Processing: It is a data pipeline component that decides the data flow implementation.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

phData: Data Engineering

AUGUST 4, 2023

Data storage is a vital aspect of any Snowflake Data Cloud database. Within Snowflake, data can either be stored locally or accessed from other cloud storage systems. What are the Different Storage Layers Available in Snowflake? They are flexible, secure, and provide exceptional performance.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

ProjectPro

AUGUST 24, 2021

Data Engineering Project for Beginners If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of data engineering project examples below. This big data project discusses IoT architecture with a sample use case.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Cloudera

MARCH 31, 2021

One of our customers, Commerzbank, has used the CDP Public Cloud trial to prove that they can combine both Google Cloud and CDP to accelerate their migration to Google Cloud without compromising data security or governance. . Data Preparation (Apache Spark and Apache Hive) .

Google Cloud 90

Google Cloud Cloud Amazon Web Services Cloud Storage 90

Knowledge Hut

SEPTEMBER 26, 2023

Born out of the minds behind Apache Spark, an open-source distributed computing framework, Databricks is designed to simplify and accelerate data processing, data engineering, machine learning, and collaborative analytics tasks. This flexibility allows organizations to ingest data from virtually anywhere.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

Rockset

MARCH 15, 2021

While there’s typically some amount of data engineering required here, there are ways to minimize it. For example, instead of denormalizing the data, you could use a query engine that supports joins. This will avoid unnecessary processing during data ingestion and reduce the storage bloat due to redundant data.

MongoDB 40

MongoDB Data Ingestion Analytics Application Kafka 40

Confluent

OCTOBER 16, 2019

Trains are an excellent source of streaming data—their movements around the network are an unbounded series of events. Using this data, Apache Kafka ® and Confluent Platform can provide the foundations for both event-driven applications as well as an analytical platform. As with any real system, the data has “character.”

Kafka 19

Kafka Building Data Coding 19

AltexSoft

JULY 29, 2022

To facilitate data ingestion, there are Apache Flume aggregating log data from multiple servers and Apache Sqoop designed to transport information between Hadoop and relational (SQL) databases. Yet, its pool of supporters definitely stands out if compared with other Big Data platforms. Hadoop ecosystem evolvement.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

MAY 31, 2021

Data Description: You will use the Covid-19 dataset(COVID-19 Cases.csv) from data.world , for this project, which contains a few of the following attributes: people_positive_cases_count county_name case_type data_source Language Used: Python 3.7 Big Data Project using Hadoop with Source Code for Web Server Log Processing 5.

Big Data 52

Big Data Coding Project Hadoop 52

Monte Carlo

AUGUST 19, 2023

We’ll cover: What is a data platform? Recently, there’s been a lot of discussion around whether to go with open source or closed source solutions (the dialogue between Snowflake and Databricks’ marketing teams really brings this to light) when it comes to building your data platform.

Building 52

Building BI Data Lake Data Governance 52

AltexSoft

MARCH 30, 2023

Databricks architecture Databricks provides an ecosystem of tools and services covering the entire analytics process — from data ingestion to training and deploying machine learning models. Besides that, it’s fully compatible with various data ingestion and ETL tools. Let’s see what exactly Databricks has to offer.

Scala 64

Scala Data Lake BI Google Cloud 64