In today’s world, data analytics is playing a vital role in the success of almost all businesses. To analyze your big data sets, you may end up spending a lot on complex hardware and software.
Cloud offerings around data warehouse and analytics can relieve you from these pains with cloud service providers offering tools that make it easy to store your big data sets.
These tools also allow you to perform advanced analytics on the data sets. Google offers BigQuery whereas Amazon offers RedShift as cloud-based enterprise data warehouse options for large-scale data analytics. Both of these services take real-time data analytics for bigger businesses to new heights.
Google BigQuery and Amazon Redshift are both cloud services and process data in real time. The biggest advantage of using them is that they do not require any initial investment on hardware and software. However, the decision to choose one out of these cloud services can be cumbersome.
The following factors play a crucial role in deciding which cloud offering is more suitable for your business:
- Fully managed
In case of Amazon Redshift, you need to define the type of servers as well as the number of server instances you want to use in the cluster. This means you need to have a fair understanding of hardware limits to envision the scale-up and scale-out.
On the other hand, Google BigQuery is a server-less data warehouse and does not require you to define any infrastructure. For example, you don’t have to think about how many server instances you need or what should be the configuration of these instances (such as CPUs, RAM, etc.).
For Amazon Redshift, you need to pay on an hourly basis for each server you provision, even when the servers are idle. Compute charges will additionally be applicable for the use of server instances.
When you use Google BigQuery, you’re charged for the queried data rather than the number of server instances used to process the queries. However, it is important to note that Google BigQuery does not use database indexes and your query might scan the entire database. So the cost depends upon how many queries you are executing and how much data is getting scanned by these queries, making it difficult to estimate the cost at the end of the month. Also, costs can fluctuate based on query patterns.
The speed or performance of query execution largely depends on the number of CPUs. When you use Amazon Redshift, you get the number of CPUs you are paying for. To keep the performance high, you need to define how to distribute data among the server instances. Redshift allows data indexing, so you can define indexes for the queries you want to run fast. All this may require a database administrator who can look into these database tasks.
On the other hand, Google BigQuery automatically and intelligently brings in the number of CPUs to run your query as quickly as possible (in seconds). There is no concept of indexing in BigQuery; each query runs fast. This is managed internally by Google by forming a massively parallel distributed tree for pushing down a query to the tree followed by aggregating results from the leaves at a fast speed. Besides this, storing data in columnar storage helps in achieving high compression ratio and scanning throughout.
The three factors above help us conclude that BigQuery scores over Amazon Redshift in almost all of these instances.
Amazon Redshift is cost-effective and gives you the opportunity to analyze and optimize queries; Google BigQuery scores higher in terms of simplicity. BigQuery keeps you away from underlying hardware, database and any kind of configuration. It eliminates the need to understand the complexities involved in data indexing, vacuuming (for periodic maintenance), distributing data among servers, server sizing, and maintaining scale-up and scale-down. Apart from scanning a lesser volume of data, BigQuery also eliminates the need to think about the infrastructure and allows you to fully focus on analyzing the data.
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