ProjectPro

NOVEMBER 14, 2015

Big data has taken over many aspects of our lives and as it continues to grow and expand, big data is creating the need for better and faster data storage and analysis. These Apache Hadoop projects are mostly into migration, integration, scalability, data analytics, and streaming analysis. Data Migration 2.

Hadoop 52

Hadoop Project Big Data Healthcare 52

ProjectPro

FEBRUARY 21, 2023

Apache Spark is the most efficient, scalable, and widely used in-memory data computation tool capable of performing batch-mode, real-time, and analytics operations. The next evolutionary shift in the data processing environment will be brought about by Spark due to its exceptional batch and streaming capabilities.

Scala 52

Scala Programming Language Java Hadoop 52

LinkedIn Engineering

APRIL 4, 2023

System Architecture Overview Setup We wanted to build a single data processing pipeline that would be efficient and scalable as more metrics are added. The data needed to compute our metrics came from various sources including MySQL databases, Kafka topics and Hadoop (HDFS).

MySQL 122

MySQL Datasets Software Engineer Software Engineering 122

Knowledge Hut

SEPTEMBER 6, 2023

Data Analysis : Strong data analysis skills will help you define ways and strategies to transform data and extract useful insights from the data set. Big Data Frameworks : Familiarity with popular Big Data frameworks such as Hadoop, Apache Spark, Apache Flink, or Kafka are the tools used for data processing.

Big Data 52

Big Data Certification Hadoop Scala 52

Booking.com Engineering

DECEMBER 2, 2022

Our team collectively runs more than 1 million queries per month, scanning more than 2 PB of data. BigQuery saves us substantial time — instead of waiting for hours in Hive/Hadoop, our median query run time is 20 seconds for batch, and 2 seconds for interactive queries[3]. It runs as a managed service, with minimal maintenance.

Systems 52

Systems Cloud MySQL Relational Database 52

AltexSoft

MAY 12, 2023

A fixed schema means the structure and organization of the data are predetermined and consistent. It is commonly stored in relational database management systems (DBMSs) such as SQL Server, Oracle, and MySQL, and is managed by data analysts and database administrators. Hadoop, Apache Spark).

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Ascend.io

SEPTEMBER 14, 2023

PySpark, for instance, optimizes distributed data operations across clusters, ensuring faster data processing. Use Case: Transforming monthly sales data to weekly averages import dask.dataframe as dd data = dd.read_csv('large_dataset.csv') mean_values = data.groupby('category').mean().compute()

Data Engineering 52

Data Engineering Data Engineer Python Engineering 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. Flat File A type of database that stores data in a plain text format.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

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

Knowledge Hut

JANUARY 24, 2024

Tableau also provides flexible data refresh options, enabling me to schedule and manage data updates according to my preferences. Real-time Data Processing Power BI supports real-time data processing, a feature I find valuable for working with live data and obtaining immediate insights.

BI 98

BI Business Intelligence Non-relational Database Data Analysis 98

AltexSoft

OCTOBER 21, 2022

popular SQL and NoSQL database management systems including Oracle, SQL Server, Postgres, MySQL, MongoDB, Cassandra, and more; cloud storage services — Amazon S3, Azure Blob, and Google Cloud Storage; message brokers such as ActiveMQ, IBM MQ, and RabbitMQ; Big Data processing systems like Hadoop ; and. Kafka vs Hadoop.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93

ProjectPro

FEBRUARY 16, 2023

According to the 8,786 data professionals participating in Stack Overflow's survey, SQL is the most commonly-used language in data science. Despite the buzz surrounding NoSQL , Hadoop , and other big data technologies, SQL remains the most dominant language for data operations among all tech companies.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52

Knowledge Hut

MARCH 20, 2024

Builds and manages data processing, storage, and management systems. Average Salary: $1,25,648 (Source: Glassdoor) Required skills: Strong programming abilities in Python, Java, Scala, or other programming languages are essential for a data engineer. Authorization and user authentication across servers and systems.

Software Engineer 52

Software Engineer Software Engineering Engineering Java 52

AltexSoft

NOVEMBER 7, 2022

Airflow is especially useful for orchestrating Big Data workflows. Airflow is not a data processing tool by itself but rather an instrument to manage multiple components of data processing. For production purposes, choose from PostgreSQL 10+, MySQL 8+, and MsSQL. When Airflow won’t work. Airflow scheduler.

PostgreSQL 52

PostgreSQL Metadata Python MySQL 52

Knowledge Hut

MAY 2, 2024

As per Apache, “ Apache Spark is a unified analytics engine for large-scale data processing ” Spark is a cluster computing framework, somewhat similar to MapReduce but has a lot more capabilities, features, speed and provides APIs for developers in many languages like Scala, Python, Java and R. billion (2019 - 2022).

Scala 52

Scala Hospitality Healthcare Retail 52

Knowledge Hut

JUNE 16, 2023

Data modeling and database management: Data analysts must be familiar with DBMS like MySQL, Oracle, and PostgreSQL as well as data modeling software like ERwin and Visio. Cloud computing: For data analysts, familiarity with cloud computing platforms like AWS, Azure, and Google Cloud Platform is crucial.

Programming Language 52

Programming Language Cloud Computing Data Preparation Data Science 52

U-Next

AUGUST 3, 2022

The Big Data age in the data domain has begun as businesses cope with petabyte and exabyte-sized amounts of data. Up until 2010, it was extremely difficult for companies to store data. Now that well-known technologies like Hadoop and others have resolved the storage issue, the emphasis is on information processing.

Retail 52

Retail Data Science Computer Science Entertainment 52

Rockset

JANUARY 23, 2020

While traditional RDBMS databases served well the data storage and data processing needs of the enterprise world from their commercial inception in the late 1970s until the dotcom era, the large amounts of data processed by the new applications—and the speed at which this data needs to be processed—required a new approach.

Database 40

Database MySQL Kafka NoSQL 40

ProjectPro

JULY 27, 2021

Data Engineer Interview Questions on Big Data Any organization that relies on data must perform big data engineering to stand out from the crowd. But data collection, storage, and large-scale data processing are only the first steps in the complex process of big data analysis.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Databand.ai

DECEMBER 13, 2022

Follow Charles on LinkedIn 3) Deepak Goyal Azure Instructor at Microsoft Deepak is a certified big data and Azure Cloud Solution Architect with more than 13 years of experience in the IT industry. On LinkedIn, he focuses largely on Spark, Hadoop, big data, big data engineering, and data engineering.

Data Engineering 52

Data Engineering Data Engineer Engineering AWS 52

AltexSoft

OCTOBER 8, 2021

The tool supports all sorts of data loading and processing: real-time, batch, streaming (using Spark), etc. ODI has a wide array of connections to integrate with relational database management systems ( RDBMS) , cloud data warehouses, Hadoop, Spark , CRMs, B2B systems, while also supporting flat files, JSON, and XML formats.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

ProjectPro

FEBRUARY 18, 2022

Anyone who works with data, whether a programmer, a business analyst, or a database developer, creates ETL pipelines , either directly or indirectly. ETL is a must-have for data-driven businesses. The transition to cloud-based software services and enhanced ETL pipelines can ease data processing for businesses.

Project 52

Project AWS Kafka Healthcare 52

Databand.ai

DECEMBER 20, 2022

Follow Eric on LinkedIn 10) Brian Femiano Senior Data Engineer at Apple Brian is a senior data engineer with nearly two decades of experience at companies like Booz Allen Hamilton, Magnetic, Pandora, and, most recently, Apple. She works to connect business with technology, bring data insights into decision-making, and mentor analysts.

Data Analytics 52

Data Analytics Google Cloud Data Science Data Mining 52

ProjectPro

AUGUST 24, 2021

Learn how to process Wikipedia archives using Hadoop and identify the lived pages in a day. Utilize Amazon S3 for storing data, Hive for data preprocessing, and Zeppelin notebooks for displaying trends and analysis. Understand the importance of Qubole in powering up Hadoop and Notebooks.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

ProjectPro

MARCH 19, 2015

Big Data NoSQL databases were pioneered by top internet companies like Amazon, Google, LinkedIn and Facebook to overcome the drawbacks of RDBMS. RDBMS is not always the best solution for all situations as it cannot meet the increasing growth of unstructured data.

NoSQL 49

NoSQL Big Data SQL Database-centric 49

ProjectPro

DECEMBER 7, 2021

Big data pipelines must be able to recognize and process data in various formats, including structured, unstructured, and semi-structured, due to the variety of big data. Over the years, companies primarily depended on batch processing to gain insights. However, it is not straightforward to create data pipelines.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

ProjectPro

APRIL 26, 2016

Table of Contents Hadoop Hive Interview Questions and Answers Scenario based or Real-Time Interview Questions on Hadoop Hive Other Interview Questions on Hadoop Hive Hadoop Hive Interview Questions and Answers 1) What is the difference between Pig and Hive ? Usually used on the server side of the hadoop cluster.

Hadoop 40

Hadoop Metadata SQL Database 40

U-Next

JULY 2, 2022

The cloud computing model delivers computing resources on-demand – that is, through the Internet – such as data storage, compute power and data processing. Introduction To Cloud Engineer Skills. Understanding how cloud storages work is one of the most important skills you can learn as a cloud engineer. Conclusion.

Cloud 40

Cloud Engineering Cloud Computing Amazon Web Services 40

AltexSoft

AUGUST 29, 2023

These are the most organized forms of data, often originating from relational databases and tables where the structure is clearly defined. Common structured data sources include SQL databases like MySQL, Oracle, and Microsoft SQL Server. Semi-structured data sources. Video explaining how data streaming works.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

ProjectPro

JULY 23, 2021

Real-time Data Processing Application 7. Real-time Data Processing Application The goal is to process the high-volume data quantities in real-time with no compromises on the accuracy of the outcomes. Ace your Big Data engineer interview by working on unique end-to-end solved Big Data Projects using Hadoop.

AWS 52

AWS Project Amazon Web Services Cloud Computing 52

ProjectPro

AUGUST 31, 2018

Running services in the cloud involve data processing that needs to be saved at some time. However, Azure’s storage capabilities are also highly reliable.Both AWS and Azure are strong in this category and include all the basic features such as REST API access 3and server-side data encryption.

AWS 45

AWS Cloud Amazon Web Services Cloud Computing 45