Exploring the Apache ecosystem for data analysis

April 11, 2024

The Apache Software Foundation develops and maintains open source software projects that significantly impact various domains of computing, from web servers and databases to big data and machine learning. As the volume and velocity of time series data continue to grow, thanks to IoT devices, AI, financial systems, and monitoring tools, more and more companies will rely on the Apache ecosystem to manage and analyze this kind of data.

This article provides a brief tour of the Apache ecosystem for time series data processing and analysis. It will focus on the FDAP stack—FlightDataFusionArrow, and Parquet—as these projects particularly affect the transport, storage, and processing of large volumes of data.

How the FDAP stack enhances data processing

The FDAP stack brings enhanced data processing capabilities to large volumes of data. Apache Arrow acts as a cross-language development platform for in-memory data, facilitating efficient data interchange and processing. Its columnar memory format is optimized for modern CPUs and GPUs, enabling high-speed data access and manipulation, which is beneficial for processing time series data.

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Apache Parquet, on the other hand, is a columnar storage file format that offers efficient data compression and encoding schemes. Its design is optimized for complex nested data structures and is ideal for batch processing of time series data, where storage efficiency and cost-effectiveness are critical.


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DataFusion leverages both Apache Arrow and Apache Parquet for data processing, providing a powerful query engine that can execute complex SQL queries over data stored in memory (Arrow) or in Parquet files. This integration allows for seamless and efficient analysis of time series data, combining the real-time capabilities of InfluxDB with the batch processing strengths of Parquet and the high-speed data processing capabilities of Arrow.

Specific advantages of using columnar storage for time series data include:

  • Efficient storage and compression: Time series data typically consist of sequences of values recorded over time, often tracking multiple metrics simultaneously. In columnar storage, data is stored by column rather than by row. This means that all values for a single metric are stored contiguously, leading to better data compression because consecutive values of a metric are often similar or change gradually over time, making them highly compressible. Columnar formats like Parquet optimize storage efficiency and reduce storage costs, which is particularly beneficial for large volumes of time series data.
  • Improved query performance: Queries on time series data often involve aggregation operations (like SUM, AVG) over specific periods or metrics. Columnar storage allows for reading only the columns necessary to answer a query, skipping irrelevant data. This selective loading significantly reduces I/O and speeds up query execution, making columnar databases highly efficient for the read-intensive operations typical of time series analysis.
  • Better cache utilization: The contiguous storage of columnar data improves CPU cache utilization during data processing. Because most analytical queries on time series data process many values of the same metric simultaneously, loading contiguous column data into the CPU cache can minimize cache misses and improve query execution times. This is particularly beneficial for time series analytics, where operations over large data sets are common.

A seamlessly integrated data ecosystem

Leveraging the FDAP stack alongside InfluxDB facilitates seamless integration with other tools and systems in the data ecosystem. For instance, using Apache Arrow as a bridge enables easy data interchange with other analytics and machine learning frameworks, enhancing the analytical capabilities available for time series data. This interoperability helps build flexible and powerful data pipelines that can adapt to evolving data processing needs.

For example, many database systems and data tools have started supporting Apache Arrow to leverage its performance benefits and become part of the community. Some notable databases and tools in this camp include:

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  • Dremio: Dremio is a next-generation data lake engine that integrates directly with Arrow and has been an early adopter of Arrow Flight SQL. It uses Arrow Flight to enhance its query performance and data transfer speeds.

Read the full story, via InfoWorld.

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