Monte Carlo

NOVEMBER 21, 2024

This approach is fantastic when you’re not quite sure how you’ll need to use the data later, or when different teams might need to transform it in different ways. It’s more flexible than ETL and works great with the low cost of modern data storage. The data lakehouse has got you covered!

Data Pipeline 52

Data Pipeline Designing Lambda Architecture Kafka 52

ProjectPro

JUNE 6, 2025

So, let’s dive into the list of the interview questions below - List of the Top Amazon Data Engineer Interview Questions Explore the following key questions to gauge your knowledge and proficiency in AWS Data Engineering. Become a Job-Ready Data Engineer with Complete Project-Based Data Engineering Course !

Data Engineering 40

Data Engineering Data Engineer Engineering NoSQL 40

ProjectPro

JUNE 6, 2025

The normalization process helps in: removing redundant data (for example, storing data in multiple tables) and ensuring data integrity. Normalization is useful for minimizing data storage and logically storing data in multiple tables. List some of the benefits of data modeling.

Data Warehouse 53

Data Warehouse NoSQL PostgreSQL Relational Database 53

ProjectPro

JUNE 6, 2025

With global data creation expected to soar past 180 zettabytes by 2025, businesses face an immense challenge: managing, storing, and extracting value from this explosion of information. Traditional data storage systems like data warehouses were designed to handle structured and preprocessed data.

Data Lake 40

Data Lake Building Hadoop Raw Data 40

ProjectPro

JUNE 6, 2025

Microsoft offers Azure Data Lake, a cloud-based data storage and analytics solution. It is capable of effectively handling enormous amounts of structured and unstructured data. Therefore, it is a popular choice for organizations that need to process and analyze big data files.

Data Lake 40

Data Lake Amazon Web Services Data Warehouse Big Data 40

ProjectPro

JUNE 6, 2025

This means that a data warehouse is a collection of technologies and components that are used to store data for some strategic use. Data is collected and stored in data warehouses from multiple sources to provide insights into business data. Data from data warehouses is queried using SQL.

Data Lake 40

Data Lake Data Warehouse Cloud Hadoop 40

ProjectPro

JUNE 6, 2025

Table of Contents What is Real-Time Data Ingestion? For this example, we will clean the purchase data to remove duplicate entries and standardize product and customer IDs. They also enhance the data with customer demographics and product information from their databases.

Data Ingestion 40

Data Ingestion Kafka Google Cloud AWS 40

ProjectPro

JUNE 6, 2025

An ETL (Extract, Transform, Load) Data Engineer is responsible for designing, building, and maintaining the systems that extract data from various sources, transform it into a format suitable for data analysis, and load it into data warehouses, lakes, or other data storage systems.

Data Engineering 40

Data Engineering Data Engineer Engineering ETL Tools 40

ProjectPro

JUNE 6, 2025

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. Calcite has chosen to stay out of the data storage and processing business.

Big Data 49

Big Data Project Metadata Programming Language 49

ProjectPro

JUNE 6, 2025

AWS Data Engineering is one of the core elements of AWS Cloud in delivering the ultimate solution to users. AWS Data Engineering helps big data professionals manage Data Pipelines, Data Transfer, and Data Storage. Table of Contents Who is an AWS Data Engineer? What Does an AWS Data Engineer Do?

AWS 45

AWS Data Engineering Data Engineer Amazon Web Services 45

ProjectPro

JUNE 6, 2025

Netflix Analytics Engineer Interview Questions and Answers Here's a thoughtfully curated set of Netflix Analytics Engineer Interview Questions and Answers to enhance your preparation and boost your chances of excelling in your upcoming data engineer interview at Netflix: How will you transform unstructured data into structured data?

Data Engineering 40

Data Engineering Data Engineer Engineering NoSQL 40

ProjectPro

JUNE 6, 2025

DynamoDB's low latency and automatic scaling capabilities make it a good choice for high-traffic applications that require fast and reliable access to data. However, MongoDB can perform well for complex queries and can handle a variety of data types, including unstructured and semi-structured data.

NoSQL 40

NoSQL MongoDB Database Amazon Web Services 40

ProjectPro

JUNE 6, 2025

Step 2: Understanding the Basics of Big Data Once you are familiar with a programming language, it is time to start learning the basics of big data. Familiarize yourself with concepts like distributed computing, data storage, and data processing frameworks. Learn how Spark distributes tasks and manages resources.

Programming Language 40

Programming Language Scala Hadoop Big Data 40

ProjectPro

JUNE 6, 2025

A data warehouse is a data storage system that collects data from various sources to provide meaningful business insights. It is like a central location where quality data from multiple databases are stored. Amazon Redshift is a fully-managed cloud data warehouse solution offered by Amazon.

Data Warehouse 40

Data Warehouse Data Mining Google Cloud PostgreSQL 40

ProjectPro

JUNE 6, 2025

Traditional databases are great at handling structured data, like text or numerical values, but they struggle with high-dimensional vector data. Compared to traditional methods that are efficient for structured data, HNSW is helpful in retrieving similar vectors (e.g., Pinecone is helpful in this situation.

Database 40

Database Project Metadata Unstructured Data 40

ProjectPro

JUNE 6, 2025

Furthermore, BigQuery supports machine learning and artificial intelligence, allowing users to use machine learning models to analyze their data. BigQuery Storage BigQuery leverages a columnar storage format to efficiently store and query large amounts of data. Q: Which two services does BigQuery provide?

Bytes 40

Bytes Google Cloud Data Warehouse Cloud Storage 40

ProjectPro

JUNE 6, 2025

PySpark SQL and Dataframes A dataframe is a shared collection of organized or semi-structured data in PySpark. This collection of data is kept in Dataframe in rows with named columns, similar to relational database tables. With PySparkSQL, we can also use SQL queries to perform data extraction.

Big Data 40

Big Data Data Process Process Kafka 40

ProjectPro

JUNE 6, 2025

These AWS resources offer the highest level of usability and are created specifically for the performance optimization of various applications using content delivery features, data storage, and other methods. AWS Redshift Amazon Redshift offers petabytes of structured or semi-structured data storage as an ideal data warehouse option.

AWS 40

AWS Data Engineering Data Engineer Engineering 40

ProjectPro

JUNE 6, 2025

The cloud data storage handle all data transportation and transformation. Self-hosted IR : This service is required to coordinate actions between cloud data stores and a private network data store. The project will require you to use Azure Data Factory to mass import raw and structured data into Azure.

Data Engineering 40

Data Engineering Data Engineer Engineering Hospitality 40

ProjectPro

JUNE 6, 2025

By 2028, the size of the global market for data warehousing is likely to reach $51.18 The volume of enterprise data generated, including structured data, sensor data, network logs, video and audio feeds, and other unstructured data, is expanding exponentially as businesses diversify their client bases and adopt new technologies.

Data Warehouse 40

Data Warehouse Engineering Business Intelligence Google Cloud 40

ProjectPro

JUNE 6, 2025

Relational Databases Relational databases form the backbone of modern data storage and management systems, powering various applications across industries. Gaming Platforms: DynamoDB is an ideal solution for building gaming platforms with features like player data storage, session history, and leaderboards.

AWS 40

AWS Database Amazon Web Services MySQL 40

ProjectPro

JUNE 6, 2025

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. Data Variety Hadoop stores structured, semi-structured and unstructured data.

Big Data 40

Big Data Hadoop Relational Database AWS 40

ProjectPro

JUNE 6, 2025

This allows organizations to centralize and democratize data across teams through easy access to information for business intelligence. In this project, you will learn how Redshift performs data storage and analysis with the help of the Amazon Redshift and Quicksight business intelligence tools. What is Amazon Redshift used for?

Data Pipeline 64

Data Pipeline AWS Project Building 64

ProjectPro

JUNE 6, 2025

Spark saves data in memory (RAM), making data retrieval quicker and faster when needed. Spark is a low-latency computation platform because it offers in-memory data storage and caching. PySpark SQL is a structured data library for Spark. VertexId is just an alias for Long. Discuss PySpark SQL in detail.

Hadoop 68

Hadoop Metadata Java Datasets 68

ProjectPro

JUNE 6, 2025

Apache Hive Architecture Apache Hive has a simple architecture with a Hive interface, and it uses HDFS for data storage. Data in Apache Hive can come from multiple servers and sources for effective and efficient processing in a distributed manner. Spark SQL, for instance, enables structured data processing with SQL.

Hadoop 45

Hadoop Java Big Data Tools Big Data 45

ProjectPro

JUNE 6, 2025

Project Idea : Build a data engineering pipeline to ingest and transform data, focusing on runs, wickets, and strike rates. Use the ESPNcricinfo Ball-by-Ball Dataset to process match data. Store raw data in AWS S3, preprocess it using AWS Lambda, and query structured data in Amazon Athena.

Data Engineering 59

Data Engineering Data Engineer Project Engineering 59

ProjectPro

JUNE 6, 2025

Its Thrift interface acts as a bridge for third-party tools to access Hive metadata, enhancing data management capabilities. Hive Query Language (HiveQL) HiveQL is a query language in Apache Hive designed for querying and analyzing structured data stored in Hadoop, especially in HDFS. What is Hive design?

Big Data 40

Big Data Architecture Project Hadoop 40

ProjectPro

JUNE 6, 2025

You can easily connect to multiple data sources, manipulate data, and load it into different data storage systems using Python. This makes it an ideal choice for ETL developers, data engineers , and data analysts, even those without a strong programming background.

Python 40

Python ETL Tools Data Warehouse Programming Language 40

ProjectPro

JUNE 6, 2025

Data Model Structured data with tables and columns. Semi-structured data in JSON format. Use Cases Best for traditional relational database use cases with structured data. Best for unstructured, semi-structured, or variable data with high throughput and scalability needs.

Amazon Web Services 40

Amazon Web Services NoSQL Relational Database AWS 40

ProjectPro

JUNE 6, 2025

FAQs on Data Engineering Skills Mastering Data Engineering Skills: An Introduction to What is Data Engineering Data engineering is the process of designing, developing, and managing the infrastructure needed to collect, store, process, and analyze large volumes of data. 2) Does data engineering require coding?

Data Engineering 49

Data Engineering Data Engineer Engineering Hadoop 49

Snowflake

APRIL 2, 2025

Data storage has been evolving, from databases to data warehouses and expansive data lakes, with each architecture responding to different business and data needs. Traditional databases excelled at structured data and transactional workloads but struggled with performance at scale as data volumes grew.

Data Lake 95

Data Lake Cloud Storage Metadata Data Warehouse 95

ProjectPro

JUNE 6, 2025

Below are some big data interview questions for data engineers based on the fundamental concepts of big data, such as data modeling, data analysis , data migration, data processing architecture, data storage, big data analytics, etc. Structured data usually consists of only text.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

JUNE 6, 2025

Might have comparatively constrained flexibility in data management and querying, relative to traditional databases. Graph Databases Focus on representing and structuring relationships between entities. Employ graph structures for data storage and querying, enabling efficient navigation through relationships.

Database 40

Database Algorithm Machine Learning Metadata 40

ProjectPro

JUNE 6, 2025

Let us compare traditional data warehousing and Hadoop-based BI solutions to better understand how using BI on Hadoop proves more effective than traditional data warehousing- Point Of Comparison Traditional Data Warehousing BI On Hadoop Solutions Data Storage Structured data in relational databases.

Hadoop 40

Hadoop BI Big Data Business Intelligence 40

Monte Carlo

MARCH 3, 2025

Prior to data powering valuable data products like machine learning models and real-time marketing applications, data warehouses were mainly used to create charts in binders that sat off to the side of board meetings. The most common themes: Data readiness- You cant have good AI with bad data.

Unstructured Data 52

Unstructured Data Data Banking Cloud Computing 52

ProjectPro

JUNE 6, 2025

Big data is often characterized by the seven V's: Volume , Variety , Velocity, Variability, Veracity, Visualization, and Value of data. Big data engineers leverage big data tools and technologies to process and engineer massive data sets or data stored in data storage systems like databases and data lakes.

Big Data 45

Big Data Big Data Skills Hadoop Scala 45

ProjectPro

JUNE 6, 2025

Understanding of continuous data protection methods. Familiarity with Snowflake Cloud Data Platform, SQL queries, and database design. Knowledge of handling unstructured and semi-structured data. Excellent problem-solving and analytical skills.

Certification 40

Certification Data Pipeline Hadoop AWS 40