I want to highlight a new presentation about Data Preparation in Data Science projects:
“Comparison of Programming Languages, Frameworks and Tools for Data Preprocessing and (Inline) Data Wrangling in Machine Learning / Deep Learning Projects”
A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 80% of the whole project.
This session compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing (like Talend, Pentaho), streaming analytics ingestion (like Apache Storm, Flink, Apex, TIBCO StreamBase, IBM Streams, Software AG Apama), and data wrangling (DataWrangler, Trifacta) within visual analytics. Various options and their trade-offs are shown in live demos using different advanced analytics technologies and open source frameworks such as R, Python, Apache Hadoop, Spark, KNIME or RapidMiner. The session discusses how this is related to visual analytics tools (like TIBCO Spotfire). Therefore, it also shows best practices for how the data scientist and business analyst should work together to build good analytic models.
Key takeaways of this session:
– Learn various options for preparing data sets to build analytic models
– Understand the pros and cons and the targeted persona for each option
– See different technologies and open source frameworks for data preparation
– Understand the relation to visual analytics and streaming analytics, and how these concepts are actually leveraged to build the analytic model after data preparation
Slide Deck
The following shows the slide deck:
You are currently viewing a placeholder content from Default. To access the actual content, click the button below. Please note that doing so will share data with third-party providers.
Video Recording: Data Preparation vs. (Inline) Data Wrangling
Here is the video recording:
Every enterprise is being told to go agentic. Meanwhile, the platforms holding your most critical…
AI agents fail in production when they are connected directly to raw event streams. Flink…
Complex Event Processing is the most underused capability in Apache Flink. It detects meaningful event…
MCP, REST/HTTP APIs, and Apache Kafka are not alternatives. They solve different problems at different…
The Enterprise Agentic AI Landscape 2026 maps every major AI vendor across two dimensions that…
Agentic AI without governed processes is fast but ungoverned. Event-driven integration without process intelligence moves…