How to Become a Data Engineer: 9 Steps, Key Skills, and Career Prospects for 2025

Companies rely on data engineers to build scalable infrastructure for real-time analytics, AI, and cloud computing. With businesses processing petabytes of data daily, handling this volume efficiently requires Apache Spark for high-speed processing, AWS for cloud scalability, and SQL for structured data management. 

These tools enable fraud detection in finance, personalized recommendations in e-commerce, and predictive analytics in healthcare.

This guide explores industry trends, career opportunities for data engineers, and the key skills required in 2025. You'll get a step-by-step roadmap, essential technologies, and job market strategies to advance in this high-demand field.

How to Become a Data Engineer: 9 Essential Steps

Businesses generate over 328.77 million terabytes of data daily, increasing demand for scalable, high-performance data infrastructure. Real-time analytics, AI-assisted decision-making, and IoT applications require efficient data pipelines that ensure reliability, security, and speedTheir work ensures:

• Financial institutions detect fraud by processing millions of transactions per second using Kafka and Spark .
• E-commerce platforms enhance personalization by integrating real-time data streams into recommendation engines.
• Healthcare and AI industries rely on structured datasets for predictive modeling and automation.

As 90% of enterprises are integrating multi-cloud storage and analytics platforms, expertise in AWS, Azure, and Google Cloud is crucial. Understanding SQL optimization, distributed processing, and cloud automation is essential for anyone learning how to become a data engineer in 2025.

But how do data engineers compare to data scientists? The table below outlines their key differences and industry roles.

For example, in high-frequency trading, data engineers develop low-latency data pipelines that process millions of transactions per second. Data scientists then apply predictive models to detect anomalies and mitigate financial risks.

Now, let’s explore the nine essential steps to becoming a data engineer in 2025.

1. Develop Strong Programming Skills

Data engineers must write efficient, scalable, and optimized code to process massive datasets, automate workflows, and integrate diverse data sources. Mastering multiple languages ensures compatibility across big data frameworks and enterprise systems. 

Here’s a list of major programming languages and where they are used:

• Python: Used for ETL (Extract, Transform, Load) processes, scripting, and data manipulation.  Netflix automates video recommendation models using Python and Apache Airflow to clean and process real-time streaming data.
• SQL: Essential for querying relational databases, handling structured data, and optimizing performance. Financial institutions use SQL to detect fraudulent transactions by analyzing customer spending patterns in real-time.
• Scala: Preferred for big data processing with Apache Spark due to its speed and parallel computing capabilities. Uber uses Scala with Spark to process millions of ride requests per minute, optimizing surge pricing.
• Java: Common in Hadoop-based architectures for enterprise-scale data platforms. LinkedIn relies on Java and Hadoop to process billions of user interactions and personalize content recommendations.
• Bash and Shell scripting: Help automate data ingestion and transformation in cloud environments. AWS writes shell scripts to move logs from S3 to a Redshift data warehouse, enabling faster analytics.

Industry Insight: Student developers use Python more than SQL (59% vs. 37%), while professional developers use SQL more than Python (52% vs. 45%). This indicates the dominance of SQL in real-world data engineering roles.

2. Gain Expertise in Databases

A data engineer’s primary responsibility is ensuring efficient data storage, retrieval, and optimization across multiple platforms. Industries handling high-volume, real-time data face the most significant challenges:

• E-commerce platforms must track millions of inventory updates and customer interactions across global warehouses.
• Financial institutions rely on real-time fraud detection, requiring instant transaction validation across secure databases.
• AI-driven businesses process unstructured data from multiple sources, demanding scalable, high-speed storage solutions.

To address these challenges, data engineers work with specialized database systems:

• Relational Databases (SQL-based): PostgreSQL, MySQL, and AWS RDS store structured data, enabling fast and accurate transaction processing.
  • Example: Retail companies use PostgreSQL to track real-time inventory updates across multiple locations, ensuring accurate stock levels.
• NoSQL Databases: MongoDB and Cassandra handle semi-structured and unstructured data, making them ideal for real-time applications, log storage, and personalized recommendations.
  • Example: Facebook uses Cassandra to store and manage messenger chat history, ensuring fast retrieval at scale.
• Columnar Databases: Amazon Redshift and Google BigQuery optimize big data analytics performance, processing large-scale queries faster than traditional row-based databases.
  • Example: Spotify uses BigQuery to analyze billions of song streams, improving user playlist recommendations.
• Performance Optimization Techniques: Indexing, partitioning, and caching reduce query latency, ensuring sub-second response times in real-time analytics.
  • Example: Fintech startups can optimize PostgreSQL indexing to detect fraudulent credit card transactions within seconds, preventing unauthorized purchases.

Organizations in finance, e-commerce, and AI automation depend on high-performance databases to manage real-time customer transactions, fraud detection, and large-scale recommendation engines.

Also Read: Credit Card Fraud Detection Project – Machine Learning Project

3. Learn Data Warehouse Architecture

A data warehouse consolidates information from multiple sources, ensuring fast, accurate, and structured access for decision-making, trend analysis, and predictive modeling. Mastering data warehouse architecture helps data engineers design scalable, cost-effective storage solutions. Here’s why: 

• Cloud Data Warehouses (Snowflake, BigQuery, AWS Redshift) provide on-demand scalability to handle petabyte-scale workloads without on-premise infrastructure limitations.
  • Example: Airbnb uses Snowflake to store and analyze millions of guest reviews, helping improve customer experiences.
• Star and Snowflake Schema Designs improve query efficiency by reducing redundant data storage and enhancing data retrieval speeds.
  • Example: Telecom companies implement Snowflake Schema to optimize billing data retrieval, ensuring quick access for millions of users.
• ETL Pipelines transform raw, unstructured data into clean, structured formats by performing:
  • Data Extraction – Pulls data from multiple sources (databases, APIs, logs).
  • Data Transformation – Cleans, formats, and applies business logic to ensure consistency.
  • Data Loading – Transfers optimized datasets into a structured warehouse for analysis.
  • Example: Healthcare providers use AWS Glue ETL pipelines to clean and aggregate patient records, improving diagnostic predictions.
• Columnar Storage and Data Partitioning enhance query performance, reducing processing times for high-volume data operations.
  • Example: A media streaming company partitions its viewership data in Redshift, reducing query times for advertisers analyzing engagement trends.

60% of enterprises have migrated to cloud-based data warehouses, making expertise in data modeling and storage optimization essential for future data engineers.

Also Read: Data Warehouse Architecture: Everything You Need to Know

4. Master Hadoop for Large-Scale Data Processing

As companies process terabytes of real-time data, traditional storage and processing systems struggle with speed, fault tolerance, and scalability. Hadoop solves these challenges by:

• Handling petabyte-scale storage with HDFS, enabling businesses like Twitter to store and retrieve billions of user interactions daily for real-time trend analysis.
• Executing large-scale computations with MapReduce, allowing global banks to process millions of daily transactions and detect fraud in near real-time.
• Providing cost-effective big data processing, reducing infrastructure costs compared to high-maintenance relational databases.

Here are the key components of Hadoop that every data engineer must master:

• Scale storage efficiently with HDFS (Hadoop Distributed File System): Unlike traditional file systems, HDFS stores data across multiple servers, minimizing bottlenecks and ensuring fault tolerance.
  • Example: Twitter stores and retrieves billions of user interactions daily from HDFS for real-time analytics.
• Accelerate batch data processing with MapReduce: This framework enables parallel execution, allowing large datasets to be processed faster and more efficiently.
  • Example: A global bank processes millions of transactions daily using MapReduce to detect fraud patterns in near real-time.
• Simplify big data queries with Hive and Pig: These tools allow engineers to write SQL-like queries and perform complex transformations without deep programming knowledge.
  • Example: E-commerce companies use Hive to analyze customer purchase behavior and optimize inventory forecasting.
• Enable real-time lookups with HBase: Unlike batch processing tools, HBase supports millisecond-level data retrieval, making it ideal for real-time applications.
  • Example: Facebook Messenger relies on HBase to store and retrieve messages instantly, processing billions of interactions per second.

Industry Insight: The Hadoop market was valued at $26.74 billion in 2019 and is projected to reach $340.35 billion by 2027, growing at a CAGR of 37.5%. Despite the rise of cloud-based data solutions, Hadoop remains essential for industries handling high-throughput, scalable data processing.

Also Read: What is the Future of Hadoop? Top Trends to Watch

5. Learn Machine Learning Integration for Data Pipelines

As AI adoption accelerates, data engineers play a crucial role in preparing and optimizing data for machine learning applications. Poor data quality and inefficient pipelines are leading causes of AI project failures, making ML-integrated data engineering a must-have skill.

Here’s what data engineers must understand about machine learning integration:

• Ensure high-quality data preprocessing for AI models: Raw datasets often contain missing values, duplicates, and inconsistencies that impact model accuracy.
  • Example: Healthcare AI systems removes duplicate patient records before training a disease prediction model, improving accuracy.
• Transform raw data into meaningful features: Feature engineering improves model interpretability and predictive performance, helping businesses extract valuable insights.
  • Example: Fintech companies create spending behavior features from transaction logs, improving credit risk prediction models.
• Deploy machine learning models at scale with MLOps: Data engineers integrate ML models into real-time data pipelines, ensuring they stay updated and efficient.
  • Example: Uber’s demand forecasting model dynamically updates using real-time ride request data, optimizing pricing strategies.
• Make use of distributed AI processing with Apache Spark ML: Spark ML enables parallel machine learning computations on big data, reducing training time.
  • Example: Retailers use Spark ML to analyze customer behavior across millions of transactions, optimizing marketing campaigns.

Key Insight: 70% of AI projects fail due to poor data quality. Data engineers bridge this gap by ensuring clean, high-integrity pipelines for AI workloads, making this an in-demand skill.

6. Optimize Performance Across Multiple Operating Systems

Data engineering requires working across Linux, Windows, macOS, and cloud-based platforms. Each system has specific configurations, performance optimizations, and security measures that impact data workflows.

While Linux dominates cloud and big data environments due to efficiency, security, and open-source flexibility, Windows Server remains critical in financial institutions and enterprises for SQL Server databases, Active Directory management, and compliance-driven infrastructure.

These are the critical OS competencies every data engineer needs:

• Master Linux for cloud and big data environments: Most enterprise-grade data infrastructure runs on Linux due to its efficiency, security, and scalability.
  • Example: AWS, Google Cloud, and Azure rely on Linux-based virtual machines for high-performance data storage and analytics.
• Understand Windows-based enterprise data solutions: Many financial institutions and corporate environments use Windows for SQL Server and Microsoft-based data applications.
  • Example: A major bank runs its SQL Server-based risk analysis system on Windows due to compliance requirements.
• Use macOS for local development and containerized workflows: Many engineers prefer macOS for running Docker containers and developing ETL scripts.
  • Example: A data engineering team at a startup develops and tests ETL pipelines locally on macOS before deploying to AWS Lambda.
• Work with cloud-based VMs for scalable data engineering workflows: Engineers configure and manage Google Compute Engine, AWS EC2, and Azure Virtual Machines for handling cross-platform data pipelines.
  • Example: An e-commerce platform deploys its customer analytics pipeline across multiple cloud-based VMs to ensure global performance consistency.

Industry Impact: Acing different types of OS environments allows data engineers to deploy, troubleshoot, and optimize workflows seamlessly across hybrid cloud and on-premise architectures, boosting job market competitiveness.

7. Earn Professional Certifications

Certifications validate industry-standard skills and increase job market visibility, helping professionals secure higher-paying roles in cloud-based and big data engineering. 

• Certifications signal hands-on proficiency: Many certification programs include real-world projects, case studies, and hands-on labs, ensuring candidates can apply their skills in real-world settings.
• Recruiters use certifications as a filtering criterion: In a job market flooded with applicants, companies often prioritize candidates who hold industry-recognized certifications, especially for cloud-native data engineering roles.

To meet the growing industry demand, data engineers should focus on certifications covering cloud platforms, big data processing, and AI-driven analytics:

• upGrad’s Data Science Courses: Covers Python, SQL, data visualization, machine learning, and big data concepts, making it a solid foundation for aspiring data engineers.
  • A learner completing upGrad’s data engineering modules builds a real-time ETL pipeline in AWS, helping secure a cloud data engineer role.
• Google Professional Data Engineer: Validates skills in Google Cloud Platform (GCP), big data processing, and AI-driven analytics.
  • Example: A certified Google Cloud Data Engineer can make use of GCP-based recommendation models for personalized playlists.
• AWS Certified Data Analytics – Specialty: Specializes in Redshift, Glue, and Kinesis, essential for cloud-based data warehousing.
  • Fintech startups hire AWS-certified engineers to build fraud detection pipelines using Kinesis and Lambda.
• Microsoft Certified: Azure Data Engineer Associate: Focuses on Azure Synapse, Cosmos DB, and real-time analytics.
  • Example: A Microsoft-certified engineer designs a data lake on Azure for a healthcare provider managing millions of patient records.
• Databricks Certified Data Engineer: Specializes in Apache Spark for distributed data processing and machine learning integration.
  • Example: A Databricks-certified engineer optimizes supply chain forecasting for a global logistics company.

Industry Insight: According to Global Knowledge’s IT Skills Report, professionals with cloud and data engineering certifications earn 20-25% higher salaries than non-certified peers.

8. Sharpen Your Problem-Solving Abilities

Data engineers work in high-stakes environments, where inefficient data pipelines, slow queries, and system failures can cost businesses millions. The ability to identify, debug, and optimize complex data workflows is critical to success.

Here’s how to strengthen your problem-solving skills as a data engineer:

• Optimize ETL pipelines to prevent bottlenecks: Poorly designed ETL processes lead to slow data transformation, inconsistencies, and failures in production systems.
  • Example: Netflix engineers restructured their Apache Airflow-based ETL jobs, reducing data processing time and improving streaming analytics efficiency.
• Debug and monitor real-time data pipelines: Unexpected traffic spikes, hardware failures, or API downtimes can disrupt data flow. Engineers must implement monitoring tools to ensure continuous data processing.
  • Example: Uber’s data team uses real-time monitoring dashboards to detect data pipeline failures affecting surge pricing models.
• Improve database performance with indexing and partitioning: Slow query execution impacts user experience, reporting, and real-time applications.
  • Example: Amazon engineers optimize their PostgreSQL indexing, cutting search response times in half for millions of product queries.
• Build fault-tolerant data architectures: Distributed systems must handle failures gracefully to ensure high availability and minimal downtime.
  • Example: LinkedIn engineers implement Apache Kafka replication, allowing continuous data streaming without service interruptions.

9. Engage with Industry Communities and Stay Informed

With rapid advancements in cloud computing, AI, and big data, staying updated is crucial.  Active participation in industry forums, open-source projects, and expert-led discussions can help professionals expand their network, showcase technical expertise, and attract job opportunities.

Here’s how to stay engaged and grow as a data engineer:

• Join active data engineering communities and forums: Networking with professionals exposes you to real-world problem-solving techniques and hiring opportunities.
  • Example: r/dataengineering on Reddit and LinkedIn’s Data Engineering Community discuss best practices and latest trends.
• Follow industry leaders and thought-provoking blogs: Learning from experienced professionals helps you anticipate industry shifts and stay ahead. Must-follow experts and resources:
  • Wes McKinney (creator of Pandas) – Shares insights on Python-based data processing.
  • Martin Kleppmann (author of Designing Data-Intensive Applications) – Covers distributed systems and real-time data architectures.
  • Data Engineering Weekly (newsletter) – Curates trends, tools, and industry research.
  • Google Cloud & AWS Blogs – Provide updates on cloud-native data processing solutions.
• Contribute to open-source projects and build a GitHub portfolio: Hands-on experience with real-world tools enhances job prospects and technical expertise.
  • Example: Contributing to Apache Airflow or Apache Spark showcases your ability to work with production-grade data workflows.
• Attend industry events, webinars, and hackathons: Conferences and workshops connect you with hiring managers and expose you to emerging trends.
  • Example: Google Cloud’s Data Engineering Summit offers insights from top tech companies building real-time data architectures.

The next section explores the most in-demand technical and analytical skills, ensuring you’re equipped to thrive in the competitive job market and take on high-impact data engineering roles.

In-Demand Skills for Data Engineers in 2025

The global big data and analytics market is projected to reach $103 billion by 2027, reflecting the growing reliance on data-driven decision-making across industries. This surge in adoption has led to increased demand for data engineers, but the specific skill requirements vary by sector:

• Finance & Fintech: Requires expertise in real-time fraud detection, risk modeling, and high-frequency trading platforms, using tools like Apache Kafka and Spark Streaming.
• Healthcare & Biotech: Focuses on processing structured medical records, genomic sequencing, and AI-driven diagnostics, requiring skills in ETL pipelines and secure data storage solutions.
• E-Commerce & Retail: Demands personalization engines, inventory forecasting, and recommendation algorithms, emphasizing distributed databases and machine learning integration.
• Cloud Computing & AI: Prioritizes scalable infrastructure for massive data processing, necessitating experience with cloud-based data lakes and serverless computing.

The following high-value skills will set you apart in the data engineering job market.

1. Proficiency in Programming Languages

Data engineers develop, optimize, and automate workflows that handle terabytes of structured and unstructured data. Strong programming skills enable high-performance ETL pipelines, real-time data streaming, and AI-driven analytics.

Here’s how the most in-demand programming languages power real-world data engineering solutions:

• Python – The foundation of ETL and data automation
  • Why it’s essential: Used for scripting, data processing, and workflow automation with frameworks like Pandas, NumPy, and Apache Airflow.
  • Real-world example: Airbnb engineers use Python and Apache Airflow to automate data ingestion from millions of property listings, optimizing search rankings and pricing algorithms.
• SQL – The core language for data transformation
  • Why it’s essential: Enables complex queries, joins, indexing, and real-time data processing for structured data.
  • Real-world example: A global bank runs SQL-based fraud detection algorithms, flagging suspicious transactions within milliseconds, preventing financial losses.
• Java & Scala – The backbone of big data frameworks
  • Why they’re essential: Essential for distributed computing frameworks like Apache Spark, Hadoop, and Kafka that handle real-time data processing.
  • Real-world example: Netflix’s recommendation engine processes terabytes of streaming data daily using Scala and Apache Spark, delivering personalized content recommendations in real time.
• Bash & Shell Scripting – Automating data workflows
  • Why it’s essential: Automates data transfers, batch processing, and system monitoring in Linux-based environments.
  • Real-world example: Uber engineers use Bash scripts to move GPS and ride data in real time, ensuring accurate dynamic pricing.

2. Expertise in Databases and SQL

With organizations processing billions of records daily, efficient data storage and retrieval are critical. Engineers must work with both relational and NoSQL databases to support real-time analytics, AI models, and large-scale data pipelines.

Below are the must-have database skills for data engineers:

• Relational Databases (PostgreSQL, MySQL, SQL Server) – Handling structured data efficiently
  • Why they’re essential: Ensure transaction consistency, fast indexing, and efficient queries.
  • Real-world example: Amazon tracks real-time inventory across its warehouses using PostgreSQL, enabling optimized delivery logistics.
• NoSQL Databases (MongoDB, Cassandra, DynamoDB) – Optimized for unstructured, real-time data
  • Why they’re essential: Provide scalability, low-latency access, and high-speed writes for real-time applications.
  • Real-world example: Facebook’s Messenger backend stores billions of messages daily in Cassandra, ensuring instant retrieval across devices.
• Columnar Databases (Amazon Redshift, Google BigQuery, Snowflake) – Powering analytics at scale
  • Why they’re essential: Optimize big data queries, reducing execution times from minutes to seconds.
  • Real-world example: Spotify engineers use Google BigQuery to process user behavior data, refining personalized playlists and recommendations.
• Indexing, Partitioning, and Query Optimization – Improving performance for large datasets
  • Why they’re essential: Helps databases handle billions of queries with minimal latency.
  • Real-world example: Stock trading platform reduced query execution time by implementing PostgreSQL partitioning, ensuring fast order processing.

3. Hands-On Experience with Big Data Technologies

As data ecosystems evolve, new tools and frameworks are reshaping data engineering workflows. Companies now prioritize real-time processing, scalable infrastructure, and AI-optimized data pipelines, making expertise in next-gen big data technologies essential.

Here are the top big data technologies every data engineer must know:

• Apache Spark – The industry standard for high-speed data processing
  • Why it’s essential: Enables real-time data analysis, in-memory processing, and AI-based computations.
  • Real-world example: Bank of America’s fraud detection system uses Apache Spark ML to process millions of transactions per second, identifying fraud in real time.
• Hadoop Ecosystem – Managing petabyte-scale data
  • Why it’s essential: Provides distributed storage (HDFS) and scalable processing (MapReduce) for large-scale data pipelines.
  • Real-world example: Telecom providers process real-time call data records using Hadoop, optimizing network coverage and reducing dropped calls.
• Apache Kafka – Powering real-time data streaming
  • Why it’s essential: Handles high-throughput event-driven architectures for real-time analytics and AI automation.
  • Real-world example: LinkedIn tracks daily events using Kafka, delivering personalized job recommendations in seconds.
• Google Dataflow & AWS Glue – Automating cloud-native ETL pipelines
  • Why they’re essential: Enable serverless ETL, machine learning integrations, and automated batch processing.
  • Real-world example: Fintech startups automate real-time transaction data cleaning using AWS Glue, reducing fraud detection latency.

Strong programming, database management, and big data expertise open career opportunities for data engineers in high-growth industries. However, understanding which roles align with your expertise and career goals is just as crucial.

The next section breaks down the top data engineering career paths, the key responsibilities for each role, and salary expectations, helping you make informed decisions about where to focus your skills for maximum career growth.

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Top Career Opportunities for Data Engineers in 2025

Organizations prioritizing big data, AI, and cloud computing are driving new career opportunities for data engineers worldwide. Data engineers are crucial to the data science pipeline, enabling real-time analytics, AI models, and business intelligence by building scalable data infrastructures.

According to market trends, data engineering roles are among the fastest-growing tech careers. Professionals with expertise in big data technologies, cloud platforms (AWS, GCP, Azure), and ETL development are in high demand. Key trends driving growth in data engineering careers are: 

• Multi-cloud expertise is crucial – Companies are migrating to hybrid cloud solutions, increasing demand for Azure, AWS, and GCP data engineers.
• AI and automation integration – Businesses need engineers who can optimize data for AI-driven insights and real-time decision-making.
• Expanding senior-level opportunities – Lead data engineers earn over ₹29 LPA, with demand for leadership roles growing rapidly.
• Big data is a key focus – Companies handling petabyte-scale data require specialists in Apache Spark, Hadoop, and Kafka.

The table below highlights high-paying data engineering career paths, their primary responsibilities, and their average salaries based on industry data.

Source: AmbitionBox

Senior-level data engineers and cloud specialists command higher salaries, with certifications in cloud computing (AWS, GCP, Azure) increasing earning potential.

Landing a high-paying data engineering role requires more than just technical skills—it demands practical experience, industry connections, and the right learning path. Whether you’re aiming for a lead data engineer role, specializing in cloud data solutions, or breaking into big data analytics, structured learning and hands-on training can accelerate your success.

The next section explores how upGrad’s career-focused programs can help you build the expertise, portfolio, and confidence needed to secure top roles in data engineering.

How upGrad Can Help You Shape Your Data Engineering Career?

Building a successful data engineering career requires technical expertise, hands-on experience, and industry connections. If you're looking to master SQL, Python, big data technologies like Apache Spark, and cloud platforms like AWS or Azure, upGrad’s specialized programs provide a structured learning path. With 10 million+ learners, 200+ courses, and 1400+ hiring partners, upGrad ensures job-ready training with real-world applications. 

Below are industry-aligned courses designed to equip you with high-demand data engineering skills:

• Analyzing Patterns in Data and Storytelling – Learn how to interpret and visualize complex datasets for business insights.
• Data Science in E-Commerce – Understand how data engineering powers recommendation systems and customer analytics.
• Linear Algebra for Analysis – Build a strong mathematical foundation for machine learning and big data.
• Basics of Inferential Statistics – Master statistical concepts to enhance data analysis accuracy.
• Case Study Using Tableau, Python, and SQL – Gain practical experience in data visualization and real-world problem-solving.
• Post Graduate Certificate in Data Science & AI (Executive) – Get an advanced certification in data-driven AI applications.

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Explore our Popular Data Science Courses

Elevate your career by learning essential Data Science skills such as statistical modeling, big data processing, predictive analytics, and SQL!

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Reference Links:
https://news.wildintelligence.xyz/p/328-million-terabytes-of-data
https://www.cloudzero.com/blog/cloud-computing-statistics/
https://survey.stackoverflow.co/2023/
https://explodingtopics.com/blog/corporate-cloud-data
https://www.businesswire.com/news/home/20201112005841/en/Global-Hadoop-Market-2020-to-2027---by-Component-Deployment-Model-Organization-Size-and-End-user---ResearchAndMarkets.com
https://www.futurismtechnologies.com/blog/why-70-of-ai-integration-projects-fail-and-how-to-succeed/
http://globalknowledge.com/us-en/resources/resource-library/articles/2020-15-top-paying-certifications/
https://www.ambitionbox.com/profile/data-engineer-salary