ProjectPro

JUNE 6, 2025

When working with real-world data, it may only sometimes be the case that the information is stored in rows and columns. In such instances, raw data is available in the form of JSON documents, key-value pairs, etc., and is accessed by data engineers with the help of NoSQL database management systems.

Data Engineering 49

Data Engineering Data Engineer Engineering Hadoop 49

ProjectPro

JUNE 6, 2025

are all present in logical data models. The process of creating logical data models is known as logical data modeling. Prepare for Your Next Big Data Job Interview with Kafka Interview Questions and Answers 2. How would you create a Data Model using SQL commands? Briefly define a NoSQL database.

Data Warehouse 53

Data Warehouse NoSQL PostgreSQL Relational Database 53

ProjectPro

JUNE 6, 2025

Data Engineering is the secret sauce to advances in data analysis and data science that we see nowadays. Data Engineering Roles - Who Handles What? As we can see, it turns out that the data engineering role requires a vast knowledge of different big data tools and technologies.

Data Science 40

Data Science Data Engineering Data Engineer Engineering 40

ProjectPro

JUNE 6, 2025

Tools/Tech stack used: The tools and technologies used for such data pipeline management using Apache Spark are NoSQL, API, ETL, and Python. Apache Big Data Project Using Spark #4: Data Hub Creation Image Source:actian.com Business Use Case: The business case here is to create a data hub for easy consolidation of data.

Hadoop 40

Hadoop Project Big Data Scala 40

ProjectPro

JUNE 6, 2025

These DStreams allow developers to cache data in memory, which may be particularly handy if the data from a DStream is utilized several times. The cache() function or the persist() method with proper persistence settings can be used to cache data. Furthermore, it can write data to filesystems, databases, and live dashboards.

Hadoop 68

Hadoop Metadata Java Datasets 68

Big Data Tools

AUGUST 3, 2021

Release – The first major release of NoSQL database in five years! Future improvements Data engineering technologies are evolving every day. Rack-aware Kafka streams – Kafka has already been rack-aware for a while, which gives its users more confidence. No artificial multi-tenancy and so on – Snowflake does that for you!

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 52

Big Data Tools

AUGUST 3, 2021

Release – The first major release of NoSQL database in five years! Future improvements Data engineering technologies are evolving every day. Rack-aware Kafka streams – Kafka has already been rack-aware for a while, which gives its users more confidence. No artificial multi-tenancy and so on – Snowflake does that for you!

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 52

ProjectPro

JUNE 6, 2025

It has to be built to support queries that can work with real-time, interactive and batch-formatted data. Insights from the system may be used to process the data in different ways. This layer should support both SQL and NoSQL queries. Even Excel sheets may be used for data analysis.

Data Lake 40

Data Lake Data Warehouse Cloud Hadoop 40

AltexSoft

FEBRUARY 11, 2023

It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);

Data Architect 87

Data Architect Certification Generalist Programming Language 87

Knowledge Hut

DECEMBER 26, 2023

In other words, they develop, maintain, and test Big Data solutions. They use technologies like Storm or Spark, HDFS, MapReduce, Query Tools like Pig, Hive, and Impala, and NoSQL Databases like MongoDB, Cassandra, and HBase. Data scientists work on deploying algorithms to the prepared data by the data engineers.

Data Science 97

Data Science BI Business Intelligence Data Mining 97

ProjectPro

SEPTEMBER 26, 2021

Build an Awesome Job Winning Data Engineering Projects Portfoli o Technical Skills Required to Become a Big Data Engineer Database Systems: Data is the primary asset handled, processed, and managed by a Big Data Engineer. You must have good knowledge of the SQL and NoSQL database systems.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

Knowledge Hut

JUNE 23, 2023

Other Competencies You should have proficiency in coding languages like SQL, NoSQL, Python, Java, R, and Scala. You should be thorough with technicalities related to relational and non-relational databases, Data security, ETL (extract, transform, and load) systems, Data storage, automation and scripting, big data tools, and machine learning.

Data Engineering 52

Data Engineering Data Engineer Engineering Non-relational Database 52

Knowledge Hut

SEPTEMBER 6, 2023

Problem-Solving Abilities: Many certification courses provide projects and assessments which require hands-on practice of big data tools which enhances your problem solving capabilities. Networking Opportunities: While pursuing big data certification course you are likely to interact with trainers and other data professionals.

Big Data 52

Big Data Certification Hadoop Generalist 52

ProjectPro

APRIL 27, 2016

Innovations on Big Data technologies and Hadoop i.e. the Hadoop big data tools , let you pick the right ingredients from the data-store, organise them, and mix them. Now, thanks to a number of open source big data technology innovations, Hadoop implementation has become much more affordable.

Hadoop 40

Hadoop Big Data Technology Kafka 40

Knowledge Hut

JUNE 20, 2023

Languages Python, SQL, Java, Scala R, C++, Java Script, and Python Tools Kafka, Tableau, Snowflake, etc. Skills A data engineer should have good programming and analytical skills with big data knowledge. The ML engineers act as a bridge between software engineering and data science.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

ProjectPro

JUNE 29, 2016

As open source technologies gain popularity at a rapid pace, professionals who can upgrade their skillset by learning fresh technologies like Hadoop, Spark, NoSQL, etc. If you have not sharpened your big data skills then you will likely get the boot, as your company will start looking for developers with Hadoop experience.

Hadoop 40

Hadoop Big Data Banking Consulting 40

ProjectPro

JUNE 6, 2025

Furthermore, you will find a few sections on data engineer interview questions commonly asked in various companies leveraging the power of big data and data engineering. It involves creating a visual representation of an entire system of data or a part of it.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

JUNE 6, 2025

While data scientists are primarily concerned with machine learning, having a basic understanding of the ideas might help them better understand the demands of data scientists on their teams. Data engineers don't just work with conventional data; and they're often entrusted with handling large amounts of data.

Data Engineering 49

Data Engineering Data Engineer Engineering Certification 49

ProjectPro

NOVEMBER 14, 2015

Tools/Tech stack used: The tools and technologies used for such weblog trend analysis using Apache Hadoop are NoSql, MapReduce, and Hive. Hadoop Sample Real-Time Project #8 : Facebook Data Analysis Image Source:jovian.ai Data Description The dataset for this project is of two types: batch data and stream data.

Hadoop 52

Hadoop Project Big Data Healthcare 52

ProjectPro

JUNE 6, 2025

Data can either be ingested through batch jobs that run every 15 minutes, once every night and so on or through streaming in real-time from 100 ms to 120 seconds. ii) Data Storage – The subsequent step after ingesting data is to store it either in HDFS or NoSQL database like HBase.

Hadoop 40

Hadoop Big Data Metadata Java 40

ProjectPro

AUGUST 11, 2021

It has to be built to support queries that can work with real-time, interactive and batch-formatted data. Insights from the system may be used to process the data in different ways. This layer should support both SQL and NoSQL queries. Even Excel sheets may be used for data analysis.

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

ProjectPro

JANUARY 19, 2022

While data scientists are primarily concerned with machine learning, having a basic understanding of the ideas might help them better understand the demands of data scientists on their teams. Data engineers don't just work with conventional data; and they're often entrusted with handling large amounts of data.

Data Engineering 52

Data Engineering Data Engineer Engineering Certification 52

Databand.ai

DECEMBER 13, 2022

Deepanshu’s skills include SQL, data engineering, Apache Spark, ETL, pipelining, Python, and NoSQL, and he has worked on all three major cloud platforms (Google Cloud Platform, Azure, and AWS). He also has adept knowledge of coding in Python, R, SQL, and using big data tools such as Spark. deepanshu.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

ProjectPro

AUGUST 21, 2015

Data can either be ingested through batch jobs that run every 15 minutes, once every night and so on or through streaming in real-time from 100 ms to 120 seconds. ii) Data Storage – The subsequent step after ingesting data is to store it either in HDFS or NoSQL database like HBase.

Hadoop 40

Hadoop Big Data Metadata Java 40

ProjectPro

JULY 27, 2021

Top 100+ Data Engineer Interview Questions and Answers The following sections consist of the top 100+ data engineer interview questions divided based on big data fundamentals, big data tools/technologies, and big data cloud computing platforms.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

JUNE 6, 2025

How small file problems in streaming can be resolved using a NoSQL database. Tools/Tech stack used: The tools and technologies used for such weblog trend analysis using Apache Hadoop are NoSql, MapReduce, and Hive. You will be introduced to exciting Big Data Tools like AWS, Kafka, NiFi , HDFS, PySpark, and Tableau.

Hadoop 40

Hadoop Project Big Data Media 40

ProjectPro

APRIL 20, 2017

How small file problems in streaming can be resolved using a NoSQL database. Problem Statement In this Hadoop project, you will get to understand how to perform data analytics like a Big Data Professional in the industry. You will be introduced to exciting Big Data Tools like AWS, Kafka, NiFi, HDFS, PySpark, and Tableau.

Hadoop 40

Hadoop Big Data Coding Project 40