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I've worked with both, and BigQuery is so much easier to work with. Redshift does give you more control (to a limited extent) but it takes a lot more work and s

I've worked with both, and BigQuery is so much easier to work with. Redshift does give you more control (to a limited extent) but it takes a lot more work and specialized knowledge to perform simple tasks well. In the article you linked, they gloss over the part that takes by far the longest when working with redshift: setting up "compression, distribution keys on large tables, and sort keys on commonly filtered columns". With BigQuery, you don't really worry about that crap, or about reloading your terabytes of data if you made a mistake somewhere in the schema. You also don't worry about vacuuming, or running out of space, or taking down the database from excessive CPU usage. Do you want to have a team of people at your company whose job is just to keep Redshift running smoothly? Or do you want another team of analysts or engineers? I could see some companies being forced to use Redshift, especially if they're using S3 a lot, and I could see people saving money in some specialized use cases, but for most people BigQuery is almost certainly going to be cheaper and faster in almost every way.

jimmytucson 8y ago
> I could see people saving money in some specialized use cases, but for most people BigQuery is almost certainly going to be cheaper and faster in almost every way That’s exactly what the TPC benchmarks are designed to show: how different appliances perform under the same diverse set of generic workloads. As of 2 years ago, they show that Redshift is faster. > With BigQuery, you don't really worry about that crap The benefit this depends on your organization’s level of expertise. If you grok sorting and distribution then you can leverage those to increase performance—but it’s not a prerequisite. For example, in Redshift, when you bulk load data into a table, if the user didn’t specify a compression scheme in the table definition, Redshift will analyze the data and find a scheme that works best, and automatically apply it to the table for you. BigQuery almost certainly does something similar. The difference is, with BigQuery, you’re not invited to take part in that discussion. And you’re charged as if the data is uncompressed. Psychologically, this is a huge relief if you don’t (want to) know how compression works but rest assured you’re paying for it somehow. To draw a tired analogy, vehicles with automatic transmission still have to shift gears. If you’re driving to the grocery store, not having to worry about that is a win. But if you’re racing stock cars, you’re definitely going to want a stick.
- deleted 8y ago
  [deleted]
- vgt 8y ago
  Compute resources tend to be significantly more expensive than storage. Our approach is two-fold: - We finance compute resources required for you to ingest data into BigQuery. With Redshift, you pay for ingest directly via compute cluster consumption (again, more expensive than storage). This also increases your complexity due to on-cluster contention of resources between ingest and query. - Separation of storage and compute gives you lots of options. With BigQuery ,you don't need to attach relatively expensive compute just for the luxury of getting more storage. Spectrum helps somewhat, but ultimately with Redshift you don't even get to pay for storage - you pay for compute/storage combos. - BigQuery's Long-Term Storage is not an archival storage tier - it's a discount on storage, with identical performance and durability characteristics. At only $0.01 per GB per month. This is likely a result of origins of the two technologies. BigQuery is Dremel, written and operated by Google since 2006. Redshift purchased source code to an on-premise fork of Postgres.

jimmytucson 8y ago

> I could see people saving money in some specialized use cases, but for most people BigQuery is almost certainly going to be cheaper and faster in almost every way That’s exactly what the TPC benchmarks are designed to show: how different appliances perform under the same diverse set of generic workloads. As of 2 years ago, they show that Redshift is faster. > With BigQuery, you don't really worry about that crap The benefit this depends on your organization’s level of expertise. If you grok sorting and distribution then you can leverage those to increase performance—but it’s not a prerequisite. For example, in Redshift, when you bulk load data into a table, if the user didn’t specify a compression scheme in the table definition, Redshift will analyze the data and find a scheme that works best, and automatically apply it to the table for you. BigQuery almost certainly does something similar. The difference is, with BigQuery, you’re not invited to take part in that discussion. And you’re charged as if the data is uncompressed. Psychologically, this is a huge relief if you don’t (want to) know how compression works but rest assured you’re paying for it somehow. To draw a tired analogy, vehicles with automatic transmission still have to shift gears. If you’re driving to the grocery store, not having to worry about that is a win. But if you’re racing stock cars, you’re definitely going to want a stick.

deleted 8y ago
[deleted]
vgt 8y ago
Compute resources tend to be significantly more expensive than storage. Our approach is two-fold: - We finance compute resources required for you to ingest data into BigQuery. With Redshift, you pay for ingest directly via compute cluster consumption (again, more expensive than storage). This also increases your complexity due to on-cluster contention of resources between ingest and query. - Separation of storage and compute gives you lots of options. With BigQuery ,you don't need to attach relatively expensive compute just for the luxury of getting more storage. Spectrum helps somewhat, but ultimately with Redshift you don't even get to pay for storage - you pay for compute/storage combos. - BigQuery's Long-Term Storage is not an archival storage tier - it's a discount on storage, with identical performance and durability characteristics. At only $0.01 per GB per month. This is likely a result of origins of the two technologies. BigQuery is Dremel, written and operated by Google since 2006. Redshift purchased source code to an on-premise fork of Postgres.