Aws redshift spectrum how to#
Aws redshift spectrum portable#
Aws redshift spectrum plus#

While Parquet is a self-describing format, it is limited to the file (or class of files).

One of the benefits of this approach, other than simplifying the adaptation to unpredictable changes, is that we wrap the Parquet data with Amazon Athena. This aligns with our change management approach to dynamically adjust to upstream sources. We take this approach is taken to mitigate variations in upstream systems, normally to cope with unplanned changes.īelow is an example of Google AdWords data in Apache Parquet file format within Amazon S3:Īpache Parquet files using SNAPPY compressionĪs we indicated earlier, we version the Parquet data. Also, the data is structured in a manner that reflects our change management operations. Upstream data is organized in a manner that optimizes for that particular source. We organize Apache Parquet data pipelines according to the upstream source. Step 1: Process and Organize In Apache Parquet This helps you adapt dynamically to upstream changes (see our change management operations). You can use these as outputs to refine your internal ETL/ELT workflows. By using Athena we provide you with the ability to query the data for table or view definitions. Hybrid Approach: You operate with a model that looks like (1), but you want the benefits of (2).

Aws redshift spectrum plus#

You want us to handle all the prep, conversion, and optimizations PLUS creating, updating tables/views in Athena so you can easily plugin your SQL or BI tools. Query Parquet via Athena: You don’t care about the technical details, just knowing all the underlying efficiencies and cost optimizations are in place is enough.This includes the integration, prep, conversion, and optimizations that provide ready-made Parquet files for use on-premise, AWS, Azure, or Google Cloud. We make the process easier by doing all the pre-processing for you. Use Openbridge as a Parquet ETL or ELT Service: You have an on-premise data lake or data prep workflows.We typically see three distinct pathways when customers are looking at leveraging our Apache Parquet data pipeline service We wrote a post on why the optimization and efficiency of Parquet should be used over CSV.

Aws redshift spectrum portable#

This makes Parquet a highly portable between cloud platforms. This includes how we format and structure Apache Parquet data for use in Amazon Athena, Presto, Spectrum, Azure Data Lake Analytics, or Google Cloud. One of the benefits of Parquet is that there are a number of services that natively support the format. In this post, we highlight how we optimize across common sets of use cases for Parquet centric data pipelines. If you have not done so already, consider the use of Apache Parquet as a standard in your data lake. Your approach for storing and querying data in a lake can have a significant impact on price and performance. The Azure team also has a similar post called “Process more files than ever and use Parquet with Azure Data Lake Analytics”.

Aws redshift spectrum how to#

Things may work, but the performance and the surprise in how much an approach like that can cost is something to avoid.įor example, in an Amazon blog post, they detail 10 tips on how to best organize and format data to optimize query performance. Easy, right? While technically accurate, what data is it pointing to? What format?Īmazon, Google, and Microsoft all admit that simply tossing data into their storage services and declaring victory is a recipe for disappointment. Amazon describes getting AWS Athena running as “simply pointing to your data in Amazon S3, define the schema, and start querying using standard SQL”. Most cloud providers make everything seem really easy. How To Process, Organize and Load Your Apache Parquet Data To Amazon Athena, AWS Redshift Spectrum, Azure Data Lake Analytics or Google Cloud