The data source type. See the “Class Strings” table, below, for the strings to use for all data source types. Server: The server computer. Dbname: The database name. Schema: This setting can be useful with Teradata data sources. Cloud data warehouse pausing might not be a show stopper for many groups that can use the available snapshot work-around. The bottom line: Amazon Redshift is an affordable, industry leading, cloud data warehouse that can be easily and instantly deployed to serve small or large scale analytics workloads.
As analytics in your company graduates from a MySQL/PostgreSQL/SQL Server, a pertinent question that you need to answer is which data warehouse is best suited for you.
At Hevo, we make it easier for our customers to bring all their data to the data warehouse of their choice. Naturally, our customers come to us seeking our recommendations on choosing a data warehouse. Our customers want to know which data warehouse will give them faster query times, how much data will it be able to handle and what will it cost. The answer depends on various inputs like the size of data, the nature of use and the technical capability of users managing the warehouse.
In this post, we are going to talk about the two most popular data warehouses: Amazon Redshift and Google BigQuery. Honestly, in the Redshift vs BigQuery comparison, similarities are greater than the differences. Still, there are nuanced differences that you need to be aware of while making a choice.
![Free Free](/uploads/1/2/5/6/125676104/354539791.png)
Redshift Vs BigQuery: Performance
On many head-to-head tests, Redshift has proved to show better query times when configured and tweaked correctly. There are several benchmarks available over the internet.
Redshift Vs BigQuery: Manageability and Usability
Redshift gives you a lot more flexibility on how you want to manage your resources. This means that you get more control at the cost of some management overhead. To operate a decently sized Redshift cluster efficiently, you need a deep understanding and skill-set around warehousing concepts. For example, Redshift will expect you know about how to distribute your data across nodes and will require you to do vacuuming operations on a periodic basis.
BigQuery, on the other hand, does not expect you to manage your resources. It abstracts away the details of the underlying hardware, database, and all configurations. It mostly works out of the box.
Redshift Vs BigQuery: Pricing
In the case of Redshift, you need to predetermine the size of your cluster. That means you are billed irrespective of whether you query your data on not. Shutting down clusters when not needed is left to the user. Billing is done on an hourly usage of the cluster. This makes Redshift more costly when your query volumes are low. But, if your query volumes are higher, predictable and uniformly distributed over time Redshift may turn out to be a lot cheaper. Also, the costs are more predictable because you always know the size of your cluster.
BigQuery, on the other hand, has segregated compute resources from storage. Thus, you are only charged when you are running queries. Billing is done on the amount of data processed during queries. On the surface this pricing might seem to be cheaper but, this approach makes costs for BigQuery unpredictable and it will turn out to be more expensive than Redshift when query volumes are high.
Conclusion
Ecosystems around both Amazon Redshift and Google BigQuery are buzzing. They are being actively promoted by their respective companies and both the products work as marketed. You wouldn’t be too wrong for choosing either of them. Still, we recommend one over the other in the following scenarios:
- Redshift: When you are okay spending some time optimizing your data for fast queries- when your resource utilization is going to be fairly distributed across time and a large proportion of data being actually queried rather than just sitting in the database.
- BigQuery: When you want something that just works and don’t want to spend time tuning the database when you are okay having query response times of a few minutes and you have a lot of data that is being queried rarely.
Amazon RedShift fully managed petabyte-scale data warehouse service
Redshift is a fully managed petabyte-scale data warehouse service from Amazon. The Amazon Redshift service manages all of the work of setting up, operating, and scaling a data warehouse. These tasks include provisioning capacity, monitoring and backing up the cluster, and applying patches and upgrades to the Amazon Redshift engine.
It is designed for analytics workloads and offers seamless development and integration capabilities that can be used with existing SQL or BI tools. Based on columnar storage technology, it uses parallel and distributed queries processing models across nodes to deliver the required high performance at scale. It also provides number of automation features and tools to administration and control perspective, provisioning, configuring, monitoring, backing up, and securing a data warehouse are automated.
It is designed for analytics workloads and offers seamless development and integration capabilities that can be used with existing SQL or BI tools. Based on columnar storage technology, it uses parallel and distributed queries processing models across nodes to deliver the required high performance at scale. It also provides number of automation features and tools to administration and control perspective, provisioning, configuring, monitoring, backing up, and securing a data warehouse are automated.
Benefits :
- Fast: Amazon Redshift delivers fast query performance by using columnar storage technology to improve I/O efficiency and by parallelizing queries across multiple nodes.
- Simple: Amazon Redshift helps easily automate most of the common administrative tasks to manage, monitor, and scale data warehouse.
- Extensible: Redshift Spectrum enables one to run queries against exabytes of data in Amazon S3 as well as petabytes of data stored on local disks in Amazon Redshift, using the same SQL syntax and BI tools you use today. One can store highly structured, frequently accessed data on Redshift local disks, keep vast amounts of unstructured data in an Amazon S3 “data lake”, and query seamlessly across both.
- Scalable: Helps easily resize cluster up and down as performance and capacity needs change with just a few clicks in the console or a simple API call.
- Secure: Security is built-in. One can encrypt data at rest and in transit using hardware-accelerated AES-256 and SSL, isolate clusters using Amazon VPC and even manage keys using AWS Key Management Service (KMS) and hardware security modules (HSMs).
How does it compare to the traditional data warehouse / analytics :
Amazon Redshift uses a variety of innovations to achieve up to ten times higher performance than traditional databases for data warehousing and analytics workloads:
Columnar Data Storage: Instead of storing data as a series of rows, Amazon Redshift organizes the data by column. Unlike row-based systems, which are ideal for transaction processing, column-based systems are ideal for data warehousing and analytics, where queries often involve aggregates performed over large data sets. Since only the columns involved in the queries are processed and columnar data is stored sequentially on the storage media, column-based systems require far fewer I/Os, greatly improving query performance.
Columnar Data Storage: Instead of storing data as a series of rows, Amazon Redshift organizes the data by column. Unlike row-based systems, which are ideal for transaction processing, column-based systems are ideal for data warehousing and analytics, where queries often involve aggregates performed over large data sets. Since only the columns involved in the queries are processed and columnar data is stored sequentially on the storage media, column-based systems require far fewer I/Os, greatly improving query performance.
Advanced Compression: Columnar data stores can be compressed much more than row-based data stores because similar data is stored sequentially on disk. Amazon Redshift employs multiple compression techniques and can often achieve significant compression relative to traditional relational data stores. When loading data into an empty table, Amazon Redshift automatically samples your data and selects the most appropriate compression scheme.
![Configuring mac excel for redshift data warehouse locations Configuring mac excel for redshift data warehouse locations](/uploads/1/2/5/6/125676104/144271948.gif)
Massively Parallel Processing (MPP): Amazon Redshift automatically distributes data and query load across all nodes. Amazon Redshift makes it easy to add nodes to your data warehouse and enables you to maintain fast query performance as your data warehouse grows.
Redshift Spectrum: Redshift Spectrum enables you to run queries against exabytes of data in Amazon S3. There is no loading or ETL required. Even if you don’t store any of your data in Amazon Redshift, you can still use Redshift Spectrum to query datasets as large as an exabyte in Amazon S3. When you issue a query, it goes to the Amazon Redshift SQL endpoint, which generates the query plan. Amazon Redshift determines what data is local and what is in Amazon S3, generates a plan to minimize the amount of Amazon S3 data that needs to be read, requests Redshift Spectrum workers out of a shared resource pool to read and process data from Amazon S3, and pulls results back into your Amazon Redshift cluster for any remaining processing.
Pricing : https://aws.amazon.com/redshift/pricing/
As with all Amazon Web Services, there are no up-front investments required, and you pay only for the resources you use. Amazon Redshift lets you pay as you go. You can even try Amazon Redshift for free.