Amazon Redshift Spectrum vs Apache Impala: What are the differences?

# Introduction
This markdown explains the key differences between Amazon Redshift Spectrum and Apache Impala.

1. **Query Performance**: Amazon Redshift Spectrum can query data directly in S3 while Apache Impala requires data to be loaded into HDFS or HBase. This difference impacts the query performance as Redshift Spectrum leverages the power of both Redshift and S3 for querying, making it more efficient in some scenarios.
2. **Storage**: Redshift Spectrum does not require data to be duplicated or loaded into Redshift for querying, as it can directly access data stored in S3. On the other hand, Apache Impala needs data to be loaded into HDFS or HBase, consuming additional storage resources.
3. **Cost**: Redshift Spectrum pricing is based on the amount of data scanned from S3, while Apache Impala does not have a clear pricing model as it is open-source. This difference can impact the cost management for organizations based on their data querying patterns.
4. **Data Processing**: Redshift Spectrum relies on the Redshift query optimizer and engine for processing queries that involve S3 data, while Apache Impala uses its own distributed processing engine. This difference can result in varying performance and optimization capabilities based on the data processing requirements.
5. **Ease of Use**: Redshift Spectrum integrates seamlessly with the Redshift ecosystem, providing a familiar interface for users already using Amazon Redshift. In contrast, Apache Impala may require additional setup and configuration due to its standalone nature, which can impact the ease of use for users not familiar with the Apache Hadoop ecosystem.

In Summary, Amazon Redshift Spectrum provides a more integrated and efficient solution for querying data in S3 compared to Apache Impala, which requires data to be loaded into HDFS or HBase for processing.