Knowledge Hut

MAY 2, 2024

Why We Need Big Data Frameworks Big data is primarily defined by the volume of a data set. Big data sets are generally huge – measuring tens of terabytes – and sometimes crossing the threshold of petabytes. It is surprising to know how much data is generated every minute. billion (2019 – 2022).

Scala 96

Scala Hadoop Java Datasets 96

ProjectPro

OCTOBER 15, 2014

Pig and Hive are the two key components of the Hadoop ecosystem. What does pig hadoop or hive hadoop solve? Pig hadoop and Hive hadoop have a similar goal- they are tools that ease the complexity of writing complex java MapReduce programs. Apache HIVE and Apache PIG components of the Hadoop ecosystem are briefed.

Hadoop 52

Hadoop Java Unstructured Data SQL 52

AltexSoft

JULY 18, 2023

These seemingly unrelated terms unite within the sphere of big data, representing a processing engine that is both enduring and powerfully effective — Apache Spark. Maintained by the Apache Software Foundation, Apache Spark is an open-source, unified engine designed for large-scale data analytics. Big data processing.

Big Data 52

Big Data Data Process Process Hadoop 52

ProjectPro

JUNE 6, 2025

The three essential functions of combining Google Analytics and BigQuery include- 1) Data Manipulation BigQuery allows for data manipulation and transformation, such as filtering, joins, and aggregations, which helps to prepare the data for analysis and visualization. While a field name is optional, the type must be specified.

Bytes 40

Bytes Google Cloud Data Warehouse Cloud Storage 40

Cloudera

OCTOBER 3, 2018

First, remember the history of Apache Hadoop. Google built an innovative scale-out platform for data storage and analysis in the late 1990s and early 2000s, and published research papers about their work. The two of them started the Hadoop project to build an open-source implementation of Google’s system.

Hadoop 75

Hadoop Cloud Data Storage Machine Learning 75

ProjectPro

NOVEMBER 7, 2016

Understanding the Hadoop architecture now gets easier! This blog will give you an indepth insight into the architecture of hadoop and its major components- HDFS, YARN, and MapReduce. We will also look at how each component in the Hadoop ecosystem plays a significant role in making Hadoop efficient for big data processing.

Hadoop 40

Hadoop Architecture IT Big Data 40

Ascend.io

FEBRUARY 15, 2023

Back in 2004, I got to work with MapReduce at Google years before Apache Hadoop was even released, using it on a nearly daily basis to analyze user activity on web search and analyze the efficacy of user experiments. So in this piece, I’ll give my take on the evolution of the cloud data platform, starting way back from my days at Google.

Cloud 52

Cloud Amazon Web Services Hadoop Telecommunication 52

Ascend.io

FEBRUARY 15, 2023

Back in 2004, I got to work with MapReduce at Google years before Apache Hadoop was even released, using it on a nearly daily basis to analyze user activity on web search and analyze the efficacy of user experiments. So in this piece, I’ll give my take on the evolution of the cloud data platform, starting way back from my days at Google.

Cloud 52

Cloud Amazon Web Services Hadoop Telecommunication 52

Knowledge Hut

NOVEMBER 17, 2023

In 2006, Amazon launched AWS to handle its online retail operations. Analytics Another essential tool being offered by Amazon for a data scientist is- Amazon Athena is a query service for analyzing the data in Amazon S3 or Glacier. Amazon Kinesis aggregates and processes the streaming data in real time.

AWS 52

AWS Certification Data Science Amazon Web Services 52

Rockset

OCTOBER 19, 2018

How We Got to an Open-Source World The last decade has been a bonanza for open-source software in the data world, to which I had front-row seats as a founding member of the Hadoop and RocksDB projects. Many will point to Hadoop, open sourced in 2006, as the technology that made Big Data a thing.

MongoDB 40

MongoDB Hadoop Kafka Data Warehouse 40

ProjectPro

AUGUST 31, 2018

For big data, EBS storage is incredibly fast. Big data poses challenges for standard storage, demanding the use of premium storage. For big data, much more advanced cloud infrastructure is required. Although Azure's services are less developed for big data, they are improving.

AWS 45

AWS Cloud Amazon Web Services Cloud Computing 45

ProjectPro

JULY 23, 2021

Sentiment Analysis on Real-time Twitter Data 23. AWS Athena Big Data Project for Querying COVID-19 Data 25. Build an AWS ETL Data Pipeline in Python on YouTube Data 26. Build a Job Winning Data Engineer Portfolio with Solved End-to-End Big Data Projects. Hybrid Recommendation System 21.

AWS 52

AWS Project Amazon Web Services Cloud Computing 52

ProjectPro

JANUARY 24, 2023

The three essential functions of combining Google Analytics and BigQuery include- 1) Data Manipulation BigQuery allows for data manipulation and transformation, such as filtering, joins, and aggregations, which helps to prepare the data for analysis and visualization. While a field name is optional, the type must be specified.

Bytes 52

Bytes Google Cloud Data Warehouse Cloud Storage 52

ProjectPro

SEPTEMBER 6, 2021

On the other hand, GCP Dataflow is a fully managed data processing service for batch and streaming big data processing. Dataflow allows a streaming data pipeline to be developed fast and with lower data latency. Learn more about real-world big data applications with unique examples of big data projects.

AWS 52

AWS Amazon Web Services Google Cloud Cloud Storage 52

Knowledge Hut

NOVEMBER 2, 2023

In 2006, Amazon launched AWS from its internal infrastructure that was used for handling online retail operations. For Big data Amazon Elastic MapReduce is responsible for processing a large amount of data through the Hadoop framework. For processing and analyzing streaming data, you can use Amazon Kinesis.

Amazon Web Services 52

Amazon Web Services AWS IT Transportation 52

AltexSoft

JULY 29, 2022

Depending on how you measure it, the answer will be 11 million newspaper pages or… just one Hadoop cluster and one tech specialist who can move 4 terabytes of textual data to a new location in 24 hours. The Hadoop toy. So the first secret to Hadoop’s success seems clear — it’s cute. What is Hadoop?

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59