With the rapid rise in data engineering computing, it is important to know about the essential FAANG data engineering tools to use in 2025. Data engineering tools are used to design, create, refine, process, and maintain large sets of raw data. The processed data is used in training AI systems for market research and to improve services delivered by governments, defense, healthcare, and other sectors.
Raw data extracted from sources such as government demographic databases, point-of-sales records at retail outlets, metrological data, health and insurance records, and inventories typically have structural variations. This data has to be cleaned and transformed into a common format before it can be processed to make informed decisions.
Data engineering tools perform the critical process of cleaning, refining, and processing data. With several open source and commercial tools available, this blog examines the 10 essential data engineering tools to use in 2025. The findings will help organizations to select the appropriate tool for their data engineering tasks.
Key Takeaways
- Raw data is created in different file formats. It needs to be cleaned and errors removed before it is processed.
- Data engineering tools are available for specific tasks such as data ingestion, storage, processing and transformation, workflow orchestration, modeling and visualization, analysis, and business intelligence.
- A single tool that performs all these operations is not available, since it would be very complex.
- Data scientists typically use a tech stack made of individual tools for specific tasks, and these processes raw data sequentially.
- These tools are released by several parties. It is important that the tools in the tech stack ‘talk’ with each other and give a compatible output for processing.
- Study the task requirements, the skill level of your workers, infrastructure readiness, budget, and then decide on a stack.
What are Data Engineering Tools
Firms such as Meta, Google, Walmart, and government agencies typically generate big data in the petabytes and exabytes range. This data needs to be extracted from multiple sources, such as social media conversations, chatbots, accounting, and shipping records.
Data would be created in multiple file formats such as CSV, ASCII, XML, RAW, JSON, mzML, and proprietary formats. The data engineering tool not only cleans but also converts the raw input into a single machine learning data format, such as CSV, XML, JPEG, MP4, and any other acceptable file format.
The next section examines several important aspects of data engineering tools.
Types of data engineering tools
There is no single tool that can perform all the data engineering tasks; instead, there are several. Here, we have listed out the top 10 FAANG data engineering tools according to their functions and capabilities:
| Tool Category | Definition | Top Tools |
|---|---|---|
| Data Integration and ETL | These tools are used to extract data from various sources, transform it into a usable format, and load it into a destination system. ETL is an acronym for extract, transform, load. | Fivetran, Talend, AWS Glue, and Azure Data Factory |
| Data Storage and Warehousing Solutions | These tools store and manage large amounts of data, and are categorized as relational and non-relational databases and data warehouses. | PostgreSQL, MongoDB, Apache Cassandra, Snowflake, Amazon Redshift, Google BigQuery. |
| Data Processing Tools | These tools process large datasets, in batches or in real-time streams. | Apache Spark, Apache Kafka |
| Workflow Orchestration Tools | These tools automate, schedule, and monitor complex data pipelines and workflows. | Apache Airflow |
| Programming Languages | These are general-purpose web engineering languages and are used for building custom data engineering systems. | Python, SQL |
| Cloud Platforms | Many cloud providers offer managed services for multiple functions. | Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure |
| Data Modeling and Visualization Tools | These tools are used for structuring data and creating visual representations of data for analysis. | Tableau, Microsoft Power BI, and Dremio |
What is data cleaning?
Data cleaning is the process of detecting and removing inaccurate, incomplete, corrupted, inconsistent, or irrelevant data from a dataset. The process improves the quality and reliability of raw data and sets it up for further processing. Data cleaning may involve filling in missing values, standardizing formats, correcting errors, and eliminating duplicate or outdated information. Wrong or corrupt data, in different formats, cannot be used.
Some data quality problems are:
- Missing data: Gaps in the dataset from data entry errors, incomplete data collection, or system failures.
- Duplicate records: Identical information for the same entity, which can inflate metrics and distort analysis.
- Structural errors: Inconsistencies in formatting, such as naming conventions or incorrect capitalization. For example, “N/A” and “Not Applicable”; date formats “MM/DD/YYYY” vs. “DD-MM-YYYY”; Gender ‘M/ F/ O vs. M/ F/ T. LGBTQ”
- Inconsistent data types: When values in a field are stored in different formats, such as a mix of strings and integers, it leads to the failure of algorithms
- Outliers: These are random and abnormal variations in data that differ from other data. The variations can be caused by process and measurement errors, or mistakes in data entry. These outliers can skew statistical analysis. Example: Weight/ height of children in lbs/ cms vs. Kgs/ Feet, inches, temperature in Celsius vs. Fahrenheit.
- Irrelevant data: These are observations that do not fit the specific problem being analyzed
- Deliberate and manipulated data: Some companies or government agencies may manipulate the data to mislead investors or to present inflated growth figures to the public. However, these errors must be resolved by diligence and governance.
When data sets have a hundred records, these can be corrected manually. However, data sets for a firm like Walmart or the government have millions of records. It is not possible to correct them manually. Hence, data cleaning tools are used.
10 Essential FAANG Data Engineering Tools to Use in 2025
The previous section presented an overview of data engineering tools and listed their types. It is clear that these tools perform specialized functions and are used for specific tasks. This section examines these tools and explains their function, operations, and application.
1. AWS Glue
AWS Glue from Amazon Web Services, one of the essential FAANG data engineering tools to use in 2025, is a serverless data integration service. It is used for discovery, preparing, and combining data for analytics, machine learning, and application development tasks. It provides tools for data extraction, transformation, and loading (ETL) using serverless, scale-out Apache Spark.
Amazon Glue offers automated data discovery via crawlers and includes a data catalog for storing and managing metadata. Key features include serverless infrastructure, automatic scaling, intelligent code generation, data quality monitoring, and a schema registry for data governance.
Important components of Amazon Glue are a serverless ETL, data catalog, crawlers, data integration engines, schema registry, data quality, and intelligent code generation. It works by discovering data in data sources using a web crawler, and the refined output is prepared with a data catalog and ETL tools.
The prepared data is integrated into ETL pipelines with a serverless system to process the data. The schema registry provides data governance. Some use cases are ETL pipelines, data preparation, event-driven integration, and data quality management.
2. Azure Data Factory
It is one of the top FAANG data engineering tools to use in 2025. Azure Data Factory was released by Microsoft. It is a cloud-based data integration service that is used to create automated, data-driven workflows for moving and transforming data between on-premises and cloud data stores.
Azure Data Factory is serverless and allows no-code data flow with connectors to data sources to support Azure Databricks and HDInsight computing engines. It is used to orchestrate data movement and transformation at scale. As a result, tasks such as consolidating data, migrating SSIS packages, and integrating hybrid data for analytics and business intelligence can be smoothly carried out.
Features of Azure Data Factory are orchestration, data transformation, data integration, serverless and managed service, code-free user interface, SQL Server integration services package, and scheduling and triggers.
Use cases are ETL/ELT, data migration, data consolidation, hybrid data movement and integration, and operationalizing machine learning. Business intelligence reporting is used to ingest and transform data that is loaded to reporting tools like Power BI to obtain business insights.
3. Google BigQuery
Google BigQuery, from Google, is a completely managed, serverless, and highly scalable data warehouse on the Google Cloud Platform (GCP). Being one of the most widely used FAANG data engineering tools, it runs ultra-fast, SQL-like queries on very large petabyte-sized datasets without any extra underlying infrastructure.
BigQuery has a serverless architecture and manages all resources, hardware, and maintenance of the system. As a result, engineers can focus on analysis and generating insights, rather than on database administration tasks.
Important features of Google BigQuery are scalability with storage and computing in different layers, allowing for independent scaling. Other features are high performance, support for SQL, built-in machine learning, BigQuery, real-time analytics, and data governance and security.
Use cases of BigQuery are business intelligence, predictive analytics, log analysis, data exploration, and real-time analytics. It is possible to create models for machine learning to predict behavior and performance.
4. Amazon Redshift
Amazon Redshift, launched by Amazon Web Services, is another widely used FAANG data engineering tool in 2025. Amazon Redshift is a completely managed, cloud data warehouse service that helps to analyze petabyte-sized datasets.
Amazon Redshift allows the use of standard SQL queries and business intelligence tools. It provides online analytical processing workloads and uses a columnar storage architecture with massively parallel processing for powerful analytics.
Important features of Amazon Redshift include massively parallel processing, where SQL operations are run in parallel on several nodes to query a large dataset. It offers columnar storage for fast queries, Redshift Spectrum for direct SQL queries without the need to transform data.
Other features are Redshift serverless, zero-ETL integrations, along with AI and ML. Use cases are business intelligence, data warehousing, real-time predictive analytics, and third-party data integration, among others.
5. Apache Kafka
Amazon Kafka, released by Amazon Web Services, is a fully managed service to run Apache Kafka on AWS. It provides an easy method for building and running applications using the open-source Apache Kafka to process real-time streaming data.
One of the leading FAANG data engineering tools, Apache Kafka, is used to create real-time data pipelines. While Apache Kafka is complex to install, scale, and manage manually, the process is automated and simplified by handling the operational tasks.
Some important features of Apache Kafka include the ability to create clusters easily, focus on applications, ensure compatibility, and scale without any issues. Important benefits are resiliency, easy operation, native AWS integration, security, and cost-effectiveness.
Important use cases are building data pipelines and lakes, Real-time analytics, Database event streams, and event-driven applications. It is used to deliver applications to analyze data from user activity, IoT devices, and other sources.
6. Apache Airflow
Apache Airflow, owned by the Apache Software Foundation, features in the list of 10 essential FAANG data engineering tools to use in 2025. It is an open-source workflow orchestration platform allowing users to write programs, schedule, and monitor complex data pipelines.
Apache Airflow is created with the Python language and uses directed acyclic graphs and a web interface to set workflows as a sequence of tasks for visualization and management. It is flexible, scalable, and allows connection to several applications.
Important features of Apache Airflow are workflow as a code with Python scripts and directed acyclic graphs to represent task dependencies. It has reusable operator components and connectors for connecting to several databases and tools, scheduling, and scalability.
Some use cases are creating data engineering pipelines to orchestrate complex ETL processes. DevOps operations for automating infrastructure management. It is also used to manage the full lifecycle of machine learning models.
7. Amazon Web Services
Amazon Web Services (AWS) released by Amazon Web Services is one of the top essential FAANG data engineering tools to use in 2025. It is a cloud computing platform with components for computing, storage, and databases. Rather than having on-site infrastructure, firms rent these services from cloud-based AWS.
Important features of AWS are cloud computing, on-demand IT services, Amazon Elastic Compute Cloud, Amazon Simple Storage Service, Amazon Relational Database Service, AWS Lambda, Amazon CloudFront, Amazon VPC, and others.
AWS also provides AI ML services to train and deploy AI models for LLMs and GenAI. Amazon Bedrock is a fully managed service for foundation models, Amazon Q, a gen AI assistant, and Amazon Titan with models for image and text generation.
AWS use cases are used by all sectors to improve customer experience and productivity, increase process optimization, advance healthcare, and for personalization in retail and other services. Amazon SageMaker provides a full toolset for IDE, ML models, and others.
8. Google Cloud Platform
Google Cloud Platform (GCP) offers a modular services suite for computing, data storage, machine learning, analytics, and AI. It offers IaaS, PaaS, Saas services, virtual machines, and cloud storage.
Important features of GCP are a virtual compute machine, data storage and analytics, ML and AI, networking, serverless computing, and several services. The Google Kubernetes Engine provides an environment to create containerized applications for blockchains.
GCP provides AI services with Vertex AI for creating AI models, Cloud TPU, and Gen AI on Vertex AI. For computing services, GCE provides the Compute Engine, App Engine, and Cloud Functions. The cloud storage facility is vast, agile, and secure, and has Cloud SQL, Bigtable, and Cloud Spanner.
Use cases include web hosting, Containerization, Big Data Processing, AI Model Development with pre-trained models, and Gen AI. GCP has suites of integrated tools for quick app development and rapid prototyping to quickly create models and applications with AI Studio.
9. Tableau
Tableau, launched by Salesforce, is another leading FAANG data engineering tool. It is a visual analytics and business intelligence platform. Organizations use the application to connect, prepare, and process data to create interactive visualizations and dashboards.
Tableau allows analytics in the cloud and on-premises, and supports AI-powered insights through tools like Tableau Pulse and Tableau Agent. Important components are Tableau Cloud, Tableau Desktop, Tableau Public, Tableau Pulse, and Tableau Agent.
Even non-technical people can use Tableau. The data connectivity function connects to spreadsheets and cloud datasets. AI-powered features help to create personalized, contextual, and automated insights.
Use cases of Tableau are business intelligence to identify trends, data exploration to help data engineers query data, and data storytelling to create presentations of data. Users include sales teams, data analysts, software engineers, and general non-IT staff.
10. Microsoft Power BI
Microsoft Power BI from Microsoft is also one of the 10 essential FAANG data engineering tools to use in 2025. It is a business intelligence platform used to analyze, visualize, and share data. It helps to transform data from multiple sources into interactive reports and dashboards.
The main components of Microsoft Power BI are Power BI Service, Power BI Report Server, Power BI Desktop, and Power BI Mobile. Power Query helps in data transformation by cleaning and processing data.
Data analysis expressions are a formula language to create custom measures and columns for advanced calculations. Artificial Intelligence insights find patterns and insights. Data connectivity links to multiple data sources to obtain data.
Use cases of Microsoft Power BI include financial reports, budget analysis, risk management, fraud detection, sales performance tracking, customer segmentation, and campaign analysis. Microsoft Power BI is used in supply chain monitoring, patient data analysis, and forecasting.
Which is the Best FAANG Data Engineering Tool in 2025
The previous sections listed and examined the 10 essential FAANG data engineering tools to use in 2025. But, “which among the 10 essential FAANG data engineering tools to use in 2025 is the best?”
The answer is – there is no single data engineering tool that can perform all the tasks, because the complexity and variety of the tasks vary. IT managers need to develop a technology stack that performs specific work and where all the tools are compatible.
So, while selecting a data engineering tool, you have to focus on factors like cost, requirements, technical specifications, etc.
Learn from Experts
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Conclusions
The blog examined the types of FAANG data engineering tools, data cleaning concepts, and the 10 essential FAANG data engineering tools to use in 2025. The functions, capabilities, components, and use cases of the tools were examined.
It is essential to understand your requirements, the type of data to be processed, the output needed, and the strategic decisions to be taken before deciding on adopting a tech stack. The 10 essential FAANG data engineering tools to use in 2025 represent the best solutions available and can be considered for your organization.
FAQs: 10 Essential FAANG Data Engineering Tools to Use in 2025
Q1. What are FAANG data engineering tools?
FAANG data engineering tools are software solutions developed or used by top tech companies like Facebook (Meta), Amazon, Apple, Netflix, and Google. These tools help engineers collect, clean, store, and process massive volumes of data efficiently.
Q2. Why are data engineering tools important in 2025?
Data engineering tools are critical in 2025 because organizations rely heavily on data for decision-making, AI training, and automation. These tools simplify data handling and ensure accuracy, scalability, and faster insights.
Q3. Which is the best FAANG data engineering tool in 2025?
There’s no single “best” FAANG data engineering tool. The ideal tool depends on your specific needs—data size, processing type, cost, and existing infrastructure. Many organizations use a combination of tools like AWS Glue, Apache Airflow, Google BigQuery, and Tableau to build their data pipelines.
Q4. What is the difference between data engineering and data science?
Data engineering focuses on collecting, cleaning, and organizing raw data so that it’s ready for use. Data science uses prepared data to create models, find patterns, and make predictions. In short, data engineers build the foundation; data scientists draw insights from it.
Q5. How do data cleaning tools help in data engineering?
Data cleaning tools identify and fix errors, remove duplicates, fill in missing information, and standardize formats. This ensures data accuracy and reliability before it’s analyzed or used for training AI models.
