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What are the best data engineering tools?

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Data engineering is the process of designing, building, and maintaining data pipelines and systems that enable data analysis, machine learning, and business intelligence. Data engineering requires a combination of technical skills, such as programming, database management, cloud computing, and data integration, as well as domain knowledge, communication skills, and problem-solving abilities. To perform these tasks effectively, data engineers need to use various tools and frameworks that help them collect, store, process, and deliver data. In this article, we will explore some of the best data engineering tools that are widely used in the industry and how they can help you in your data engineering projects.

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1 Data Collection Tools

Data collection tools are used to capture, ingest, and transform data from various sources, such as web pages, APIs, sensors, logs, or streaming platforms. Apache Kafka is a distributed messaging system capable of handling high volumes of data and enabling real-time data processing and streaming. Apache NiFi is a graphical data flow management tool that automates and orchestrates data ingestion and transformation workflows. Apache Airflow is a platform for scheduling, monitoring, and managing complex data pipelines and dependencies. AWS Glue is a cloud-based service for crawling, cataloging, and preparing data for analysis and machine learning.

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Bitthal Khaitan

Cloud Data Engineer| 10 million+ views on LinkedIn posts | Healthcare enthusiast| Top Data Engineering Voice | Mentor | NOT a Content Creator OR Seller | Views are my own & not of my employer
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The sort of data you must collect, your target audience, and the purpose for which the data will be used, all play a role in the choice of data gathering technology. So requirements is a key driver. Simple web forms to complicated systems utilized for particular data capturing requirements are just a few examples of data collection technologies. Online survey platforms like SurveyMonkey and Typeform, form builders like Google Forms, Microsoft Forms, and JotForm. Another example for obtaining data from webpages, are web scraping programs like BeautifulSoup and Scrapy.

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Stuart Brown

Co-Founder at Blend | CTO at X+Y | Driving Growth Through Data, AI, Media & Cyber Security | Enabling Smarter & Safer Business Decisions
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Beautiful soup is excellent for SEO and data analyst with a Python/XML/JSON skillet. It gives you the ability to scrape extremely useful data from websites which you can use to benchmark against your competition.

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2 Data Storage Tools

Data storage tools are used to store and organize data in different formats and structures, such as relational, non-relational, or file-based. Among the best data storage tools are Amazon S3, a cloud-based object storage service that can store and access any amount of data from anywhere; Apache Hadoop, a framework that can store and process large-scale data sets using distributed computing and file systems; Apache Cassandra, a distributed database that can handle high availability, scalability, and performance for large amounts of structured and unstructured data; and MongoDB, a document-based database that can store and query data in JSON-like format and support flexible schemas and indexing.

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Bitthal Khaitan

Cloud Data Engineer| 10 million+ views on LinkedIn posts | Healthcare enthusiast| Top Data Engineering Voice | Mentor | NOT a Content Creator OR Seller | Views are my own & not of my employer
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One keeps fresh vegetables in refrigerator and other snacks / dry grocery in pantry. Similarly depending on what kind of data you have the storage tools are decided upon. Example, RDBMS might not be a great choice for telemetry data but is a great choice for ecommerce transactional data.

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Stuart Brown

Co-Founder at Blend | CTO at X+Y | Driving Growth Through Data, AI, Media & Cyber Security | Enabling Smarter & Safer Business Decisions
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Bigquery is our data storage warehouse of choice at DataTalon. It's ease of use, cheap pricing, features and interoperability with other tech make it an excellent choice as a data warehouse for SMEs.

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3 Data Processing Tools

Data processing tools are used to manipulate, analyze, and transform data using various methods and techniques, such as batch processing, stream processing, or machine learning. Some of the best data processing tools include Apache Spark, a unified analytics engine that can perform fast and scalable data processing and machine learning on large-scale data sets; Apache Flink, a stream processing framework that can handle high-throughput and low-latency data applications; TensorFlow, a platform that can create and train complex neural networks and deep learning models for various tasks; and Pandas, a library that provides easy-to-use data structures and operations for data analysis in Python.

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Stuart Brown

Co-Founder at Blend | CTO at X+Y | Driving Growth Through Data, AI, Media & Cyber Security | Enabling Smarter & Safer Business Decisions
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To determine what to use to process data you need to understand your data. What is the volume, variety, velocity, veracity and value of the data? If you are talking about big data (high volume), processing within the cloud becomes the best option. For Machine Learning, Vertex AI offers excellent features and interoperability with Google cloud products. For database ETL and querying, Bigquery offers a full suite of services. For smaller datasets not requiring cloud storage, analysis and data processing can be easily achieved with python using Jupyter Notebooks in Anaconda, or browser based Google Colab. The pandas, numpy, matplotlib and seaborn libraries offer excellent data processing and visualisation capabilities for exploration.

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4 Data Delivery Tools

Data delivery tools can be used to export, publish, or visualize data for different purposes and audiences, such as reporting, dashboarding, or decision making. Apache Superset is a web-based application that can create and share interactive data visualizations and dashboards using various data sources and charts. Tableau is a software that can connect to and analyze data from various sources and create and share stunning data visualizations and dashboards. Apache Zeppelin is a web-based notebook that can integrate and execute code from various languages and frameworks, while displaying data in interactive and collaborative ways. AWS Lambda is a cloud-based service that can run code in response to events and triggers, delivering data to various endpoints and services.

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Sahebeh D.

Data Scientist @ Frequentis
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A useful example I’ve seen is power BI from Microsoft. Which you can use various data storages for visualisation and getting insights.

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Bitthal Khaitan

Cloud Data Engineer| 10 million+ views on LinkedIn posts | Healthcare enthusiast| Top Data Engineering Voice | Mentor | NOT a Content Creator OR Seller | Views are my own & not of my employer
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Some of the top fortune companies use Tableau for reporting. Microstrategy is another tool with varied enterprise adoption. Excel in an email still works best for several executives.

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Stuart Brown

Co-Founder at Blend | CTO at X+Y | Driving Growth Through Data, AI, Media & Cyber Security | Enabling Smarter & Safer Business Decisions
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Googles Looker Studio and Microsoft Fabric (PowerBI) are both excellent data visualisation tools that have a wealth of resources available like free training courses. These both work for pretty much any structured data imaginable and you can easily find agencies and resources to assist with the build of both solutions with your existing tech stack.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best data engineering tools?

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2

3

4

5

1 Data Collection Tools

2 Data Storage Tools

3 Data Processing Tools

4 Data Delivery Tools

5 Here’s what else to consider

Data Engineering

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What are the best data engineering tools?

1

2

3

4

5

1 Data Collection Tools

2 Data Storage Tools

3 Data Processing Tools

4 Data Delivery Tools

5 Here’s what else to consider

Data Engineering

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