Apache Spark VS Airflow

Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly.

Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection.

One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a healthy balance between freedom and accountability, ultimately making it easier for developers to adapt and contribute without restrictive legal barriers. Another modern twist discussed in the article is the concept of dual licensing. Dual licensing can offer an attractive method for additional commercial exploitation while still upholding open source principles. However, as the article cautions, dual licensing involves legal intricacy and demands rigor in managing Contributor License Agreements (CLAs), a challenge that the open source community navigates with ongoing debates. For developers looking to understand similar innovative approaches to licensing, further information can be explored at License Token.

[1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache Spark: Lightning-Fast Unified Analytics Engine," Available: https://spark.apache.org/. [6] Java Community Process, "Java Machine Learning Libraries and Frameworks," Available: https://www.oracle.com/java/.

Apache Spark isn’t just a framework for distributed data processing; it’s a rich ecosystem that includes libraries for machine learning, stream processing, and graph processing. A key aspect of Spark’s ecosystem is its reliance on community contributions. Developers from around the world collaborate on its GitHub repository, ensuring that Spark remains at the cutting edge of technology. The governance process, characterized by transparency and meritocracy, builds trust among contributors and sponsors alike. An essential component of Apache Spark’s model is its use of the Apache 2.0 license. This permissive license not only shields contributors with patent protection but also allows enterprises to integrate Spark into proprietary systems without legal hurdles. The license enables a free flow of innovation—companies can both use and contribute to Spark’s codebase, leading to enhancements that benefit the entire community. The funding mechanisms sustaining Apache Spark are as diverse as they are innovative. Corporate sponsorships play a significant role, with companies dedicating resources and finances to support ongoing development. Additionally, grant programs and community donations help maintain an ecosystem where improvements and new features are continuously shared with users worldwide. These sustainable funding practices ensure that Apache Spark can meet the demands of real-time analytics and high-volume data processing.

If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline.

PySpark is the Python API for Apache Spark, an open-source distributed computing system that enables fast, scalable data processing. PySpark allows Python developers to leverage the powerful capabilities of Spark for big data analytics, machine learning, and data engineering tasks without needing to delve into the complexities of Java or Scala.

Hi HN,

We've built an SDK for building DAGs / data pipelines with LLMs in Apache Airflow [1] using Pydantic AI [2] under the hood. I've seen success across the board with Airflow users building simple LLM workflows before moving on to "AI agents". In my experience, the noise around building agents means that people forget that there are other ways to get more immediate value out of LLMs.

Coupling Airflow for orchestration and Pydantic AI for LLM interactions has turned out to be a very pragmatic approach to building these workflows (and agents). Neither tool "gets in the way" of what you're trying to do. Airflow's been around for 10+ years and has a very well-built orchestration engine rich with everything you need to write production grade data pipelines, and Pydantic AI's been a refreshing take on working with LLMs.

Would love some feedback from this community!

[1] https://github.com/apache/airflow

Apache Airflow offers simplicity when it comes to scheduling, authoring, and monitoring ML workflows using Python. The tool's greatest advantage is its compatibility with any system or process you are running. This also eliminates manual intervention and increases team productivity, which aligns with the principles of Platform Engineering tools.

Examples: https://github.com/apache/airflow/issues?q=is%3Aissue%20stat...

Other than the content (which indeed makes no sense), these usually can be recognized by subjective adjectives and polish language[1].

[1] https://news.ycombinator.com/item?id=42864854

Data orchestration tools are key for managing data pipelines in modern workflows. When it comes to tools, Apache Airflow, Dagster, and Flyte are popular tools serving this need, but they serve different purposes and follow different philosophies. Choosing the right tool for your requirements is essential for scalability and efficiency. In this blog, I will compare Apache Airflow, Dagster, and Flyte, exploring their evolution, features, and unique strengths, while sharing insights from my hands-on experience with these tools in a weather data pipeline project.

Data pipelines: Apache Kafka and Airflow are often used for building data pipelines that can continuously feed data to models in production.

This article demonstrates how to work with near real-time and historical data using the dlt package. Whether you need to scale data access across the enterprise or provide historical data for post-event analysis, you can use the same framework to provide customer data. In a future article, I'll demonstrate how to use dlt with a workflow orchestrator such as Apache Airflow or Dagster.``

This approach means the API doesn't change, i.e., you can just replace the S3CopyObjectOperator instances with S3CopyOperator instances. Additionally, we only perform the extra work of doing the multipart upload when the simpler method is insufficient. The trade-off is that we're inefficient if almost every object is larger than 5GB because we're doing a "useless" API call first. As usual, it depends. A similar approach has been discussed in this Github Issue in the Airflow repository.

helm repo add apache-airflow https://airflow.apache.org

We only use KubernetesOperators, but this has many downsides, and it's very clearly a 2nd thought of the Airflow project. It creates confusion because users of Airflow expect features A, B, and C, and when using KubernetesOperators they aren't functional because your biz logic needs to be separated. There are a number of blog posts echoing a similar critique[1]. Using KubernetesOperators creates a lot of wrong abstractions, impedes testability, and makes Airflow as a whole a pretty overkill system just to monitor external tasks. At that point, you should have just had your orchestration in client code to begin with, and many other frameworks made this correct division between client and server. That would also make it easier to support multiple languages.

According to their README: https://github.com/apache/airflow#approach-to-dependencies-o...