Artificial Intelligence and machine learning methods are always improving which is why data scientists must continuously educate themselves to stay on top of the newest developments and trends. Joining data engineering conferences, which provide opportunities to meet other industry leaders, discuss best technologies, and discover new solutions, is a terrific method.
According to your skills and sector of interest, here is a list of some of the top conferences related to data engineering, big data, and AI.
1. Subsurface Live
Website: Subsurface Live Summer
Date: 1-2 March
Location: San Francisco, London, New York
This program will cover the most recent open-source breakthroughs as well as real-world applications. Business executives from firms such as Netflix, USAA, Adobe, and Windows will speak about their expertise in designing and developing modern data lakes.
Overview of the conference:
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Keynote speeches
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Panel discussions
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Interactive workshops
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Q&A session
Who Should Attend:
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Data engineers
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Data architects
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Developers
2. International Conference on Software and Data Engineering
Website: ICSDE
Date: 6-7 March
Location: Rome, Italy
The ICSDE is a conference focused on bringing together a varied range of academic activities for presenting at the conference. Based on the number and duration of the presentations, events will take place throughout the conference. It offers outstanding value to students, scholars, and corporate researchers due to its high quality.
Overview of the conference:
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Interactive workshops
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Presentations
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Keynote speeches
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Panel discussions
Who Should Attend:
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AI technologists
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Data engineers
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Data scientists
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PhD students
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Research scientists
3. Convergence 2023
Website: Convergence
Date: 7-8 March
Location: Online
Listen from machine learning experts who have overcome developmental obstacles and successfully delivered commercial value with the technology. You'll learn about new tools, techniques, and processes that can help you efficiently control an ML project during this virtual event.
Overview of the conference:
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Testing ML productions
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Keynote speech
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Panel Discussions
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Q&A session
Who Should Attend:
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Developers
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Data engineers
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IT decision-makers
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DevOps practitioners
4. Industry of Things World USA
Website: Industry of Things World
Date: 19-21 March
Location: San Diego, California
According to the organizers, this is the most important commercial forum for industrial automation and IoT. The conference will concentrate on real-world, end-user case studies that show how intelligent manufacturing solutions are transforming organizations and sectors. Speakers from all across the world provide their perspectives on topics and current techniques.
Overview of the conference:
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Presentations
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New business service models
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Workshops: Challenge your peers
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Industry 4.0 with digitalization strategy
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Supply chain and logistics 4.0
Who Should Attend:
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IoT specialists and strategists
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IoT enthusiasts
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Cloud computing adopters
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Big data analytics experts
5. Gartner Data and Analytics Summit
Website: Gartner Data & Analytics Summit
Date: 20-22 March
Location: Orlando, Florida
The Gartner Data & Analytics Summit tackles the major difficulties that data analytics experts face as they work to create tomorrow's inventive and flexible enterprises. The conference will facilitate the development of new data and analytics techniques that allow digital acceleration, as well as delve deep into the data and analytics dynamics and tools that will change organizations.
Overview of the conference:
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Business strategy and value
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Data science and machine learning innovation
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Artificial Intelligence: Delivering value
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Emerging trends and technologies in data science
Who Should Attend:
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Chief data officers
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Chief analytics officers
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Business leaders
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Data scientists
6. Enterprise Data World
Website: Enterprise Data World
Date: 27-31 March
Location: Online
Enterprise Data World has been regarded as the biggest global educational event on managing data for the past 26 years. This year's conference will provide in-depth learning by data-driven professionals from all over the globe.
Overview of the conference:
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International council meeting
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Data strategy tutorials
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Sponsored sessions
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Data monetization workshops
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Lighting talks
Who Should Attend:
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Chief data officers
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Data and information architects
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Business intelligence and data governance
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Information quality professionals
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Big data engineers
7. NLP Summit
Website: NLP Summit
Date: 4-5 April
Location: Online
The NLP Summit brings together people who are putting cutting-edge natural language processing to practical use. This first virtual conference highlights NLP best practices, good case studies, and obstacles in implementing deep learning in practice.
Overview of the conference:
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Keynote speeches
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Panel discussions
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Workshops
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General conference sessions
Who Should Attend:
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Data scientists
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Technologists
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Big data engineers
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Chief data officers
8. Open Data Science Conference
Website: ODSC East
Date: 9-11 May
Location: Boston
ODSC East 2023 will be filled with in-person workshops as well as digital experiences accessible to anybody, anywhere. Small, engaging in-person interactions will be combined with creative and intelligent digital experiences. Participants at ODSC East will be kept up to date on the most latest and fascinating advances in data science.
Overview of the conference:
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Statistics for data science
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SQL for data science
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Painting with data: Introduction to d3.js
Who Should Attend:
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Data scientists
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Software engineers
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Analysts, managers
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CxOs
9. AI & Big Data Expo
Website: AI & Big Data Expo
Date: 17-18 May
Location: Santa Clara, California
The AI & Big Data Expo is a renowned event that demonstrates next-generation company techniques and strategies from around the world, allowing you to explore and find the functional and successful execution of AI & Big Data to propel your company forward.
Overview of the conference:
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Applied data and analytics
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Presentation: Agriculture being Fed by Data
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Keynote: Keeping systems seamless
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Presentation on big data
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Panel: Monetization of big data
Who Should Attend:
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Technologists
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Software engineers
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Developers
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Data scientists
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Business analysts
10. World Data Summit
Website: World Data Summit
Date: 17-19 May
Location: Amsterdam, Netherlands
Participants will have a better knowledge of establishing an analytical model for their company and consumer growth during this 3-day seminar. Specialists will talk on various areas of data analysis, including how to deal with unstructured data and how to improve data visualization and comprehensibility. Participants can also join a session to learn more about customer insights and ways to expand their technical skills.
Overview of the conference:
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Keynote speeches
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Panel discussions
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Q&A session
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Workshops
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Masterclass
Who Should Attend:
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Business analytic managers
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Data scientists
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Data engineers
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Data architects
11. Monitorama
Website: Monitorama
Date: 26-28 June
Location: Portland, USA
The methodologies and technologies used to manage complicated applications and infrastructure are the focus of this event. Industry professionals and local leaders will speak about the latest techniques to monitor and observe, including AIOps, to participants.
Overview of the conference:
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Keynote speeches
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Panel discussions
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Tutorials
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Interactive workshops
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Networking opportunities
Who Should Attend:
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Monitoring engineers
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Developers
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Production engineers
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Software architects and engineers
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Network architects
12. Data 2023
Website: Data 2023
Date: 11-13 July
Location: Rome, Italyl
The DATA 2023 conference will gather researchers, engineering technicians, and professionals who are interested in database systems, big data, data mining, information management, data protection, and other attributes of information systems and technology that involve data analysis applications.
Overview of the conference:
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Keynote lectures
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Workshops
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Demos and displays
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Tutorials
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Doctoral Consortium
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Industrial track
Who Should Attend:
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Data scientists
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Researchers
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Business analysts
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Data engineers
13. MLDM 2023
Website: MLDM
Date: 15-19 July
Location: New York
The goal of this event is to bring together machine learning and data mining experts from around the globe. They'll talk about the current state of recent research as well as other issues.
Overview of the conference:
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Keynote speeches
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Panel discussions
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Interactive debate
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Workshops
Who Should Attend:
- People interested in ML and data mining
14. The Responsible AI Forum
Website: The Responsible AI Forum
Date: 20-22 September
Location: Singapore
With shared stories, studies, and implementations, this 3-day conference brings together representatives of industry, society, administration, and academia to debate the most important and pressing topics linked to the appropriate use of AI.
Overview of the conference:
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Keynote speeches
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Panel discussions
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Interactive sessions
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Masterclasses
Who Should Attend:
- Anyone who is interest in AI
15. ODSC West
Website: ODSC West
Date: 31 October- 1 November
Location: To be announced
In the ODSC West Conference, some of the key contributors of many open-source programs, modules, and languages are among the lecturers. Participants will learn from some of the brightest minds in the industry about the current AI and data science subjects, techniques, and languages.
Overview of the conference:
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Keynote speakers
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Panel discussions
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Workshops
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Tutorials
Who Should Attend:
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Software engineers
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Data scientists
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Managers
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Analysts
Conclusion
Joining conferences on data engineering allow you to learn about the most recent breakthroughs and accomplishments in these technologies, which would take far too much time and energy to research and understand on your own. So, don’t miss out on these amazing data engineering conferences this year.
FAQs
What is data engineering?
Data engineers design and construct networks that modify and transmit data into a structured form that is highly usable when it reaches data scientists or other end users. These networks will collect data from various sources and store it in a single storehouse that depicts it as a single source of information.
What are data engineering conferences?
Data engineering conferences give like-minded people a chance to meet together under the same roof to discuss the latest trends and technologies that have been developed in the field of big data and data science.
Who is a data engineer?
Data engineers design technologies that gather, handle, and transform raw data into useful information for data scientists and business analysts to comprehend in a range of scenarios. Their main goal is to create data that is more available so that businesses may assess and improve their performance.
When working with data, data engineers may encounter the following tasks:
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Obtain datasets that are relevant to your company's needs.
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Create algorithms to convert data into usable insights.
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Database pipeline architectures to be built, tested, and maintained.
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Work with managers to achieve the company's goals.
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New data validation methodologies and data analysis tools must be developed.
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Ensure that data compliance and management policies are followed.
What is the need for data engineering?
The majority of businesses have undergone a digital transformation in the recent ten years. This has resulted in inconceivable numbers of new sorts of data, as well as considerably more complex data being produced at a higher rate. While it was obvious that data scientists would be required to make sense of it all, it was less obvious that someone would be required to organize and maintain the quality, safety, and accessibility of this data.
Data engineers were frequently required to develop the required infrastructure and data pipelines to accomplish their work. This wasn't necessarily part of their knowledge base or work expectations. As a result, data modeling would not be done properly.
There would be additional work and inconsistencies in data use among data engineers. Companies were unable to extract maximum value from their data initiatives as a result of these challenges, and they failed. It also resulted in a high turnover rate among data engineers, which persists today.
What is the difference between a data engineer and a data scientist?
A data engineer creates, develops, tests, and manages infrastructures like databases and huge processing technologies. A data scientist cleans, massages, and arranges data. The efforts required by both sides to extract the data into a usable format will be vastly different.
Raw data containing human, computer, or instrument faults is dealt with by data engineers. The data may not have been vetted and may contain questionable entries; it will be unformatted and may include system-specific codes. Data engineers will be responsible for recommending and, on occasion, implementing methods to increase data accuracy, efficiency, and cleanliness. To do so, they'll need to use a range of languages and methods to connect systems or look for ways to obtain fresh data from other systems.
The notion that data engineers will have to guarantee that the infrastructure in existence meets the requirements of data scientists as well as the stakeholders, and the company, is intimately tied to these two. Finally, the data engineering group will have to design data set procedures for data modeling, mining, and generation in order to give the material to the data science group.
