Google BigQuery vs Impala: What are the differences?

Introduction

Google BigQuery and Impala are both popular data processing platforms used for querying and analyzing large datasets. While they share similarities, there are some key differences between the two. The following paragraphs highlight the main distinctions between Google BigQuery and Impala.

1. Distributed Processing Model: Google BigQuery uses a fully managed serverless architecture, where the entire data processing is handled by Google infrastructure. It automatically scales resources based on the query complexity and data size, making it effortless for users to focus on analysis rather than system management. Impala, on the other hand, adopts a distributed processing model where queries are executed on a cluster of machines. Users are responsible for managing the cluster and allocating appropriate resources to ensure efficient query performance.

2. Data Storage: Google BigQuery utilizes columnar storage for data storage, known as Capacitor. It compresses and encodes data effectively, leading to significant storage savings. Impala, however, uses the Hadoop Distributed File System (HDFS) for data storage, which provides fault-tolerance and scalability. HDFS stores data in a distributed manner across multiple nodes in a cluster.

3. Query Language: Google BigQuery employs an SQL-like query language named BigQuery SQL. It supports a wide range of SQL functions and allows users to write complex queries for data analysis. Impala, on the other hand, uses a subset of SQL known as Impala SQL. Although Impala SQL is compatible with most SQL implementations, it lacks support for some advanced features and functions present in BigQuery SQL.

4. Performance Optimization: Google BigQuery automatically optimizes query execution by utilizing dynamic query optimization techniques. It intelligently chooses the best execution plans based on data statistics and parallelizes processing for large datasets. In contrast, Impala relies on metadata information provided by users to optimize query execution. Users need to analyze and modify table statistics manually to enhance performance.

5. Integration with Ecosystem: Google BigQuery seamlessly integrates with other Google Cloud Platform services, enabling easy integration with data sources, data pipelines, and machine learning services. Impala, being a part of the Apache Hadoop ecosystem, integrates well with other Hadoop components like Apache Hive, Apache HBase, and Apache Spark. This integration provides a wide range of tools and libraries for various data processing tasks.

6. Cost Model: Google BigQuery follows a pay-as-you-go pricing model. Users are billed based on the amount of data processed and storage consumed. It offers various pricing tiers to cater to different usage scenarios. Impala, being an open-source project, is free to use. However, users need to consider the cost of managing and maintaining the cluster infrastructure when opting for Impala.

In summary, Google BigQuery utilizes a serverless architecture, offers efficient data storage, automated query optimization, and extensive ecosystem integration. Impala, on the other hand, provides a distributed processing model, compatibility with Apache Hadoop components, and a cost advantage in terms of software licensing. The choice between the two depends on specific requirements, budget constraints, and the level of control desired by users.