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, 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 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 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 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 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 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.
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.