Since 2021 a leading company has significantly reduced CO2 emissions and costs
By utilizing a flexible data architecture that integrates data streams, they enable costly data processing in global locations, particularly during periods of abundant wind and sunlight
Source: We now do more computing where there’s cleaner energy by KoningsteinOne of the primary reasons AI is not a bubble is its steady progress over decades. This long history of development over almost 70 years indicates that AI isn't a passing fad but a persistent pursuit of harnessing technology for smarter, more efficient problem-solving. Another compelling reason for AI's enduring presence is its wide-ranging impact. From healthcare and finance to transportation and entertainment, AI is steadily enhancing and revolutionizing industries.
Data engineering lays the foundation upon which AI models are built, and it is an essential prerequisite for any successful AI project. And we have the toolkit you need.
Meet IntelliStream DataHub
Oliver, the data miner
Oliver, a junior software engineer, collaborates with his team to extract data from various applications and sensors. He also works with establishing relationships to map the data lifecycle and setup associations for assets, ensuring the information is accessible for future use.
Minimal data cleaning occurs due to its complexity, resulting in unrefined data being stored into DataHub. This makes it challenging to utilize this data for developing smarter real-time applications.
Laura, the data engineer
Laura, a junior software engineer, specializes in data engineering. Together with her team, she extracts data from various applications and sensors. Additionally, they assist Oliver's team in data cleaning and preparation. They employ DataHub as their primary platform for data sharing and synchronization.
Establishing relationships between assets is partially automated. The data possesses intricate and multifaceted relationships, making it challenging to derive insights without advanced data extraction tools.
Sophia the data engineer
Sophia boasts extensive expertise in data engineering. She generates new data from the information she ingests, including external sources like weather data, and engages in feature engineering. This data is processed in real-time and optimized for data scientists. Sophia communicates with Laura to understand Laura's requirements, ensuring Laura knows how best to assist her. Similarly, Sophia stays in touch with Emma to stay updated on Emma's needs and current projects.
Given the vast volume of data the company handles, Sophia's team ideally should be expanded. However, DataHub alleviates much of the intricacy associated with managing real-time data. Thanks to DataHub, the team can prioritize data engineering while spending minimal time on operations.
Emma the data scientist
Emma has experience with machine learning and deep learning. Her team is contains data scientists with background in statistics, probability, software engineering and data engineering. Their combined expertise enables them to extract valuable insights and knowledge from data.
Data modelling is one of Emma's most important tasks. Models can consume from tens to billions of data parameters, and luckily she has tools that can help her producing the additional data parameters she needs for optimizing the data models.
DataHub facilitates this process for Emma. Several measures have been implemented to guarantee that she receives high-quality and consistent data in real-time. As a result, she can allocate less time to data cleaning and more to optimize the data models.
Helen the business analyst
Helen has good domain knowledge about how the business operates. She is an expert in spreadsheets and data visualization and creates a daily report of the data that Oliver has entered into DataHub. Some of the data she uses is classified as exceeding the limit value. She writes this data back to DataHub for others to use.
Edward the risk analyst
Edward works on mapping risks in the organisation. The model he has created, uses the information found in a knowledge graph and is dependent on data that changes in real-time. This is because limit values correlate with time.
Events that Edward finds is classified and sent back to DataHub for others to use.And Tom actually subscribes on data coming from Edward.
Tom the CEO of this company
Tom is the one who is guiding the company and setting the direction. With so much information, making optimal decisions becomes challenging. However, with all data centralized in DataHub and the data lifecycle mapped alongside related data, Tom is primed to make knowledgeable decisions. Analyzing the data provides him insight into upcoming trends and pinpoints the right contacts for any questions.
IntelliStream DataHub
You can think of DataHub as a centralized repository or storage system that allows organizations to store, manage, and analyze vast amounts of raw and unstructured data. The data lifecycle is kept, so you're always in control. Data streaming is supported for building real-time data pipelines and streaming applications and is particularly well-suited for handling small and large volumes of data streams in a reliable and efficient manner. This means that everyone who consumes some data doesn't need to ask when new data is ready, but instead get the data delivered immediatly to them within a few milliseconds.
DataHub breaks down the walls of data silos, making data accessible across the entire organization. This reduces the time spent on data management and operations, allowing engineers to focus more on deriving meaningful insights from the data.
Tap on a person to see how IntelliStream DataHub works.
In the world of data, silos obstruct progress
Liberate data from silos and make them accessible for everyone in your organization. Data silos hinder collaboration and impede innovation. Valuable insights that could illuminate the big picture remain locked away, unseen by those who could leverage them. Departments operate in isolation, lacking a unified view that could guide effective decision-making. This fragmentation leads to duplicated efforts, inconsistencies, and missed opportunities.
Reveal hidden data contexts
Analysts and data scientists can incorporate multi-dimensional data-structures to infer meaning, increase machine learning accuracy and drive contextual artifical intelligence. Making better predictions with the data you already have.
DataHub comes with production-ready tools and algorithms to help you creating advanced groundbreaking machine learning workflows.
Build an "app store" for your data
Connect and process all of your data in real-time with a data streaming architecture platform, available everywhere you need it. DataHub serves as a marketplace for the constant flow of information. Much like an app store revolutionized how we access and engage with software, DataHub transforms how we interact with real-time data. Data streaming is central to the modern business environment. Move your data to an "always-on" architecture, and build applications that acts on real-time data.
Create a foundation for your data scientists
DataHub plays an important role in the success of AI projects. It involves the collection, preparation, transformation, and storage of data in a format that is suitable for analysis and modeling.
In essence, data engineering provides the foundation upon which successful machine learning models are built. Without proper data engineering practices, even the most sophisticated ML algorithms might fail to produce meaningful results due to poor data quality, inconsistency, or incompatibility with the chosen algorithms.