ProjectPro

JANUARY 25, 2022

When it comes to data ingestion pipelines, PySpark has a lot of advantages. PySpark allows you to process data from Hadoop HDFS , AWS S3, and various other file systems. PySpark SQL and Dataframes A dataframe is a shared collection of organized or semi-structured data in PySpark.

Big Data 52

Big Data Data Process Process Kafka 52

Knowledge Hut

SEPTEMBER 26, 2023

Organisations are constantly looking for robust and effective platforms to manage and derive value from their data in the constantly changing landscape of data analytics and processing. These platforms provide strong capabilities for data processing, storage, and analytics, enabling companies to fully use their data assets.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

Silectis

JANUARY 3, 2021

BI (Business Intelligence) Strategies and systems used by enterprises to conduct data analysis and make pertinent business decisions. Big Data Large volumes of structured or unstructured data. Big Query Google’s cloud data warehouse. Database A collection of structured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Scala 64

Scala Data Lake BI Google Cloud 64

ProjectPro

AUGUST 21, 2015

With the help of ProjectPro’s Hadoop Instructors, we have put together a detailed list of big data Hadoop interview questions based on the different components of the Hadoop Ecosystem such as MapReduce, Hive, HBase, Pig, YARN, Flume, Sqoop , HDFS, etc. What is the difference between Hadoop and Traditional RDBMS?

Hadoop 40

Hadoop Big Data Metadata Java 40

Monte Carlo

APRIL 24, 2023

A notebook-based environment allows data engineers, data scientists, and analysts to work together seamlessly, streamlining data processing, model development, and deployment. Databricks also pioneered the modern data lakehouse architecture, which combines the best of data lakes and data warehouses.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

AltexSoft

MAY 14, 2021

A single car connected to the Internet with a telematics device plugged in generates and transmits 25 gigabytes of data hourly at a near-constant velocity. And most of this data has to be handled in real-time or near real-time. Variety is the vector showing the diversity of Big Data. Big Data analytics processes and tools.

Big Data 78

Big Data Data Analytics IT NoSQL 78

AltexSoft

AUGUST 29, 2023

Data sources can be broadly classified into three categories. Structured data sources. These are the most organized forms of data, often originating from relational databases and tables where the structure is clearly defined. Semi-structured data sources. Video explaining how data streaming works.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? 5 Data pipeline architecture designs and their evolution The Hadoop era , roughly 2011 to 2017, arguably ushered in big data processing capabilities to mainstream organizations. Singer – An open source tool for moving data from a source to a destination.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 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

JANUARY 22, 2019

Today’s data landscape is characterized by exponentially increasing volumes of data, comprising a variety of structured, unstructured, and semi-structured data types originating from an expanding number of disparate data sources located on-premises, in the cloud, and at the edge. Data orchestration.

Big Data 44

Big Data NoSQL Data Lake Hadoop 44

ProjectPro

MAY 31, 2021

This kind of processing benefits any business that heavily relies on its website for revenue generation or to reach out to its customers. The Apache Hadoop open source big data project ecosystem with tools such as Pig, Impala, Hive, Spark, Kafka Oozie, and HDFS can be used for storage and processing.

Big Data 52

Big Data Coding Project Hadoop 52

ProjectPro

DECEMBER 7, 2021

It can also consist of simple or advanced processes like ETL (Extract, Transform and Load) or handle training datasets in machine learning applications. In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Towards Data Science

MARCH 5, 2024

The storage system is using Capacitor, a proprietary columnar storage format by Google for semi-structured data and the file system underneath is Colossus, the distributed file system by Google. Load data For data ingestion Google Cloud Storage is a pragmatic way to solve the task. Also this query comes at 0 costs.

Bytes 69

Bytes Google Cloud Cloud Storage Utilities 69

ProjectPro

JULY 22, 2016

The desire to save every bit and byte of data for future use, to make data-driven decisions is the key to staying ahead in the competitive world of business operations. All this is possible due to the low cost storage systems like Hadoop and Amazon S3.

Data Lake 40

Data Lake Data Warehouse Hadoop Unstructured Data 40

ProjectPro

NOVEMBER 22, 2021

MapReduce Apache Spark Only batch-wise data processing is done using MapReduce. Apache Spark can handle data in both real-time and batch mode. The data is stored in HDFS (Hadoop Distributed File System), which takes a long time to retrieve. You can learn a lot by utilizing PySpark for data intake processes.

Hadoop 52

Hadoop Python Datasets Metadata 52

ProjectPro

NOVEMBER 15, 2021

With SQL, machine learning, real-time data streaming, graph processing, and other features, this leads to incredibly rapid big data processing. DataFrames are used by Spark SQL to accommodate structured and semi-structured data. Hop onto the repository here: [link] 7.

Big Data 52

Big Data Project Metadata Programming Language 52