Serverless at re:Invent: The place ought to Amazon Redshift go?


A key spotlight from final week’s re:Invent was the extension of serverless compute to a swath of AWS analytics companies, together with Amazon EMR, Kinesis Information Streams, MSK (Managed Service for Kafka), and Redshift. For cloud analytics, AWS was not the primary to supply serverless choices, as Google Cloud BigQuery and Azure Synapse Analytics have lengthy provided serverless choices (in contrast, Snowflake’s continues to be in preview).

Serverless wasn’t the one new function introduced final week. AWS additionally introduced the preview of automated materialized views that deal with the creation of those views very similar to cost-based question optimizers: it mechanically generates the views primarily based on knowledge scorching spots. Nonetheless, serverless grabbed the limelight.

Whereas AWS’s serverless bulletins could possibly be seen as maintaining with the Joneses, relating to Amazon Redshift, it’s half of a bigger narrative of the info warehousing service not solely catching up however getting ready to doubtlessly bypass its rivals.

To recap, Amazon Redshift has lengthy been identified extra as a market slightly than a know-how chief. 

When AWS launched Redshift again in 2013, it was one of many first cloud knowledge warehousing companies. Beginning with know-how acquired from ParAccel, AWS profited but in addition paid the worth for being among the many first to market. Its early entry, together with the portfolio of different AWS analytics companies, enabled Redshift to carve a big consumer roster with higher than tens of 1000’s of consumers right this moment.

AWS forked the acquired ParAccel know-how. However from the get-go, it adopted a traditional knowledge warehousing structure with domestically connected storage. Against this, Google Cloud BigQuery, launched again in 2010, pioneered the cloud-native, knowledge warehouse. Nonetheless, it was the launch of Snowflake in 2014 that put the elastic cloud knowledge warehouse on the map.

For final week’s serverless announcement, the important thing improvement was the launch of RA3 situations again in 2019. They offered the long-sought elasticity with separation of computing and storage and paved the way in which for serverless. Because it seems, RA3 is the transformation that additionally allowed Redshift to do much more. Earlier this 12 months, AWS launched Superior Question Accelerator (AQUA) for Amazon Redshift that we characterised on the time as a “generational shift” that leveraged the elasticity of the RA3 situations. It was aimed toward workloads for “near-line” knowledge sitting remotely on Amazon Redshift Managed Storage, storing scorching knowledge in SSD whereas utilizing the Nitro hypervisor and FPGAs to speed up the processing of cooler knowledge sitting on S3.

By the way, in our put up final spring, we put serverless on our want record for what we needed to see subsequent. As soon as in a blue moon, we sometimes get it proper.

However there’s extra. As a result of RA3 situations pool a lot of the info in S3, that cleared the way in which for knowledge sharing, which was initially launched again within the spring for purchasers with a number of AWS accounts. At re:Invent final week, that functionality was prolonged throughout a number of areas. Once more, AWS wasn’t first to market. As an example, Snowflake has been selling varied types of knowledge sharing because it began speaking Information Sharehouse again in 2017 (they now not use that time period). AWS did launch a knowledge market (referred to as Amazon Information Change) a number of years in the past, however solely simply prolonged it to Redshift.

Let’s make a few disclaimers. To start with, do not confuse knowledge sharing with federated queries. Redshift can distant question knowledge sitting in RDS and Aurora databases for MySQL and PostgreSQL, and through Redshift Spectrum, to EMR and S3. However that is fairly much like what Google already provides with BigQuery. Secondly, do not consider that AWS is abandoning provisioned situations – it is going to maintain providing them for Redshift as properly as a result of there are prospects preferring degree billing. Google ultimately realized that when it subsequently launched flat-rate slots for BigQuery.

With cloud-native structure and serverless help, AWS has some alternatives to attain some firsts. With cloud-native serverless structure, AWS may transfer extra analytic and AI processing in-database.

However in-database machine studying has already develop into desk stakes for cloud knowledge warehouses. AWS already does so with Redshift ML, the place you need to use SQL instructions to set off growing fashions in SageMaker, then convey the fashions in-database as a type of user-defined perform (UDF) to run coaching and/or inference workloads. In flip, Google additionally supplies in-database ML for BigQuery, however it’s restricted to particular, curated fashions; whereas Microsoft permits working of ML fashions inside Azure Synapse Spark swimming pools. And with Snowpark, you need to use non-SQL languages to push down processing, resembling ML fashions, as UDFs immediately into the Snowflake database.

Our want record is to convey Spark immediately into Redshift. Right this moment, you’d have to fireplace up a separate EMR cluster to run Spark (however at the very least now, it may be triggered serverless as properly). After all, nothing is stopping AWS from breaking out Spark as a separate serverless service, simply as Google Cloud lately did. However right this moment, Azure Synapse Analytics permits you to run a curated (subset) model of Spark in-database with out firing up a separate cluster; we would wish to see AWS comply with by means of.

However let’s not cease there. Serverless additionally supplies the chance to fireplace up workloads with third-party instruments, particularly with BI reporting and visualization. Redshift at the moment has integrations with its personal QuickSight and with common instruments like Tableau, however you need to transfer knowledge and course of it in separate clusters.

So let’s lower to the chase. We might like to see AWS add a “Redshift-native” mode for third events keen to run capabilities like ELT or visualization as containerized microservices that run immediately inside Redshift RA3 compute nodes, or no matter next-generation nodes come out in future years. By comparability, Snowflake supplies frequent APIs for third events to entry Snowflake knowledge, however the knowledge is processed in separate clusters. Think about working an ELT service from Informatica or Fivetran as a microservice in a Redshift compute node. AWS may then promote Redshift as the most affordable, quickest knowledge warehouse within the cloud.

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