Kotlin for Data Analysis
Kotlin provides tools for data analysis, including Kotlin notebooks and DataFrame, enabling users to load, transform, visualize data, and integrate with databases, enhancing data science and machine learning capabilities.
Read original article
Kotlin provides a suite of tools for data analysis, essential for software developers who need to handle data in various formats. Key tools include Kotlin notebooks, Kotlin DataFrame, and Kandy, which facilitate exploratory data analysis (EDA). These tools allow users to load, transform, and visualize data from formats like CSV, JSON, and TXT directly within the IDE. Kotlin DataFrame integrates with relational databases, enabling SQL-like data manipulation and visualization. Additionally, the EDA tools can fetch and analyze real-time data from web APIs, enhancing flexibility in data handling. Kotlin notebooks, including Kotlin Notebook, Datalore, and Kotlin-Jupyter Notebook, offer interactive environments for coding, graphics, and text, allowing seamless sharing and collaboration. The Kotlin DataFrame library supports structured data manipulation, while Kandy provides a domain-specific language (DSL) for creating various charts. Together, these tools empower users to efficiently manage data analysis tasks, from retrieval to visualization, fostering skills in data science and machine learning.
- Kotlin offers various tools for data analysis, including Kotlin notebooks and Kotlin DataFrame.
- Users can load, transform, and visualize data from multiple formats directly in the IDE.
- Kotlin DataFrame allows integration with relational databases for SQL-like data manipulation.
- Kandy is a library for creating charts and visualizations, enhancing data insights.
- Kotlin notebooks support interactive coding and collaboration, facilitating data science projects.
Related
How to Use Jupyter Notebooks: The Ultimate Guide – DataCamp
Jupyter notebooks are crucial for technical and data work, offering code, output, and text in one document. DataLab, a hosted service, enhances collaboration and database connectivity. Popular among data professionals, they come in hosted and local types, with DataLab providing a user-friendly hosted option.
Self-Hosting Kusto
The article delves into self-hosting Kusto for timeseries analytics, detailing setup with the Kusto Emulator in Docker. It covers connecting, database management, data import, querying with KQL, and anomaly detection. Speed and efficiency are highlighted.
You can contribute to KDE with non-C++ code
KDE invites contributions in various programming languages and tools, including Python, Ruby, and Docker, encouraging community involvement in projects, website maintenance, and packaging technologies like Flatpak and Snap.
Show HN: COBOL-REKT, a toolkit for analysing and reverse-engineering COBOL
Cobol REKT is a toolkit for reverse engineering legacy Cobol code, offering flowchart generation, Neo4J integration, execution tracing, and static analysis, with planned features for code detection and knowledge integration.
Kedro – An open-source framework for data science code
Kedro is an open-source Python framework that enhances data science code development, offering features like pipeline visualization, a Data Catalog, and flexible deployment, successfully used by companies like Telkomsel and Beamery.
- Many users acknowledge Kotlin's strengths, particularly its expressiveness and modern features compared to Python.
- Some commenters express frustration with Python's limitations and the dominance of its ecosystem in data science.
- There are concerns about Kotlin's niche status and its reliance on the JVM, which some view as a burden.
- Users share resources and tutorials related to Kotlin's data analysis tools, indicating a growing interest in its capabilities.
- Discussions highlight the challenges of integrating Kotlin into existing tech stacks, especially when clients prefer simplicity.
11 commentsAnd it's not necessarily the most efficient path either. The interpreter is not that fast, the language is not that expressive, what passes for package management is a bad joke, etc. I can work with it but I'm not necessarily loving it.
For data engineering, Kotlin has a lot to offer. I wouldn't necessarily recommend it because it's all kind of niche. But it kind of works as well. If you aren't afraid of tinkering with it, there are a lot of other niche solutions out there as well that aren't python.
Kotlin is what I reach for when I want to get stuff done in a hurry. Part of that is just my limitation. It's what I know and I kind of grew up on JVM languages. I'm well aware that's not necessarily optimal and that that's just a bias I have. But objectively, it has a lot of nice things over modern python as well.
Kotlin is a modern language, it's a lot more expressive than python. It has a a great library ecosystem. Including some stuff that does not depend on the JVM. And even though it's kind of niche for a lot of things I use it for (e.g. developing reactive web frontends), it holds up well and rarely disappoints me.
People use python because everybody else uses python. That's it. It's not particularly good at anything it does. But it will get the job done and I can do it. But that just isn't good enough for me. It's the visual basic for data science. And that's not a compliment. Being idiot proof is it's main feature. But that doesn't make it the smart choice.
Related
How to Use Jupyter Notebooks: The Ultimate Guide – DataCamp
Jupyter notebooks are crucial for technical and data work, offering code, output, and text in one document. DataLab, a hosted service, enhances collaboration and database connectivity. Popular among data professionals, they come in hosted and local types, with DataLab providing a user-friendly hosted option.
Self-Hosting Kusto
The article delves into self-hosting Kusto for timeseries analytics, detailing setup with the Kusto Emulator in Docker. It covers connecting, database management, data import, querying with KQL, and anomaly detection. Speed and efficiency are highlighted.
You can contribute to KDE with non-C++ code
KDE invites contributions in various programming languages and tools, including Python, Ruby, and Docker, encouraging community involvement in projects, website maintenance, and packaging technologies like Flatpak and Snap.
Show HN: COBOL-REKT, a toolkit for analysing and reverse-engineering COBOL
Cobol REKT is a toolkit for reverse engineering legacy Cobol code, offering flowchart generation, Neo4J integration, execution tracing, and static analysis, with planned features for code detection and knowledge integration.
Kedro – An open-source framework for data science code
Kedro is an open-source Python framework that enhances data science code development, offering features like pipeline visualization, a Data Catalog, and flexible deployment, successfully used by companies like Telkomsel and Beamery.