The tool's most valuable feature is its parallel processing capability. It can handle massive amounts of data, even when pushing hundreds of terabytes, and its scaling capabilities are good.

The solution's performance and speed depend on the data structure and model, but Amazon Redshift's massive parallel processing is typically good. 

The main benefit is that our portal for end users is running in AWS, so we can easily connect it to other AWS services. The most important part for us is the connectivity inside AWS.

The most valuable feature is its seamless integration within the AWS ecosystem. If your data is already in S3, it becomes easier to port it into Redshift and work with it. 

Redshift allows you to transform different data formats and consolidate them into one Redshift cluster. This means you can transform various siloed data sources like Excel files and CSV files into Redshift. 

With Redshift, you get a suite of applications that enable you to drop a file and consume it into the data warehouse, facilitating the use of Power BI dashboards, Tableau dashboards, or custom dashboards on top of Redshift data. This setup process is much smoother compared to other tools that may require going through legal MSA (Master Services Agreement) and other complex procedures.

Service accounts are used in both Amazon Redshift and Google Cloud. For example, I could create a service account for my desktop to access Redshift or a service account for multiple users to access Redshift. In BigQuery, creating a service account is very simple, and you get full control over the access, so you can limit what the service account can do. This prevents accidental exposure of data or deletion of data. Only certain features are available, which is very handy.

Postgres syntax requires 25 synthetic scrubs to Postgresify. It's handy, but there are no blockers when using the query. It's more competitive, but the price is very reasonable. I was always aware of what I would pay, and if I reserved servers, I knew what it would cost. There is no alternative in choosing a solution. We had to use the server version of AWS, but it had limited features. A few features were lacking, which couldn't front Redshift against it or access it from the API. We had our nodes, which were sent from Amazon. It has a minimal setup, with two services running only. 

It was predictable because the performance was good. When a complex BBT model was running, we reached its limits. If there was a one-node setup, not all the storage was available on the server. For example, in a machine with 72 gigabytes of storage, only four were available in a single setup. I had another node, with 64Gb. All the storage of the two servers was available and when you are running these complex queries, it's not only a bit of computing but also temporarily eats up the storage. I couldn't use a single server because temporary tables ate up the storage. BigQuery’s authentication is straightforward. Besides that, it's doing what it's expected to do. There are no major problems.

The product is relatively easy to use because there is no indexing and no partitions. There is no referential integrity only declarative, which is okay.

AWS provides an ecosystem of different offerings. The product offers good support for the data lake. It also provides a lambda function for automating flows.

I have primarily used the Redshift Spectrum feature and found it most valuable.

Data Science and Machine Learning are valuable features. We have found Machine Learning use cases are very nice.

The most valuable feature of Amazon Redshift is its ability to handle really large sets of data. In our case, the solution does a lot of things that would be difficult to do otherwise.

For reporting purposes, Redshift is a great tool to use. Redshift has an advantage when it comes to administration, making it easier to manage and collaborate. Additionally, its server architecture allows for faster processing. Redshift also supports prepaid costs, which is another great feature. However, similar features are also available in Azure.

Redshift has some advantages in terms of administration and performance.

Redshift Spectrum is the most valuable feature.

The most valuable feature of Redshift is its cluster.

I like it because the usage is very similar to Microsoft SQL server. The structure of the query and the temporary tables are very similar. Until recently, I thought it was the superior database, but now I think that Redshift is better.

The TP transactions for the creation of the tables does very well.

It is quite simple to use and there are no issues with creating the tables.

For the on-premises version of Amazon Redshift, we need to start from scratch. However, with the cloud version whenever you want to deploy, you can scale up, and down, and it has a data warehousing capability. Redshift has many features.

They have enriched and elaborate documentation that is helpful.

The price isn't bad for the performance for a cloud data warehouse. It's also connected to Databricks but uses SQL. It's comparable to BigQuery. If the analyst knows SQL, which is comfortable and easy to use to go between all of these tool stacks, I think it's reliable. It's a secure and reliable data warehouse.

The performance is good, and it's pretty fast. We also have Looker and MOLE connected to it for visualization, which works seamlessly. We're storing a lot of data. There's a lot of transactional data, clickstream data, and telemetry about the customer, what they're purchasing, call logs, and marketing data.

The analysts are familiar with SQL, and they're able to do this. Even the data scientists who aren't that savvy in Python, because they are very strong in SQL, are able to interact with it very quickly. I'm able to bring in more analysts for support.

The solution works fast. I use Redshift to clear a lot of web page data. I use it mainly as an extraction tool to obtain the information I need for a project and store it in parquet files on a disk. Later, I work on the data using Python. I write back all my final results to Redshift and store the temporary files on a local machine.

Amazon Redshift was used for data storage while moving back from S3 to Amazon. However, it lagged, taking its own sweet time for data backups which also depended on the server location. Because of the aforementioned reasons, we started losing a lot of data that wasn't even real-time data. Ultimately, this affected our analytics at the end of the day. Also, we have been trying to do some work on our AI models, which emit out recommendations based on the live dataset. There were a lot of lagging issues. So, for example, sending out somewhere around 0.1 million or 100,000 emails used to take almost 12-14 working days, and this also includes the process of pulling all the data and sending them to CronJobs. Since we wanted all this work to happen in real-time, we had to get rid of the tool.

It is scalable, easy to use, and has very good query performance. It is serverless as well.

They introduced machine learning directly in to Redshift, and you can now query using machine-learning functions.

It's very easy to migrate from other databases to Redshift. There are migration tools dedicated for this purpose, enabling migration from other databases like MS SQL directly to Redshift. The majority of the scripts will be automatically transposed.

Redshift's versioning and data security are the two most critical features. When migrating into the cloud, it's vital to secure the data. The encryption and security are there. 

I find the most valuable features to be the MPP style of processing, which mostly all of the data warehouses provide. The ability to integrate all other AWS services, such as NSS and S3, with little effort is very helpful. The service is well maintained, there are update patches frequently.

I found the Amazon Redshift computing services easy. I found the computing instances the most incredible in the solution.

The most valuable feature is that the solution is fully embedded in the AWS stack. 

The product is the best database for analytics, BI, and whatever else I encounter. 

• Performance: Very fast query performance due to columnar-storage databases that leverage the Massively Parallel Processing (MPP) capabilities of its data warehouse architecture.
• Petabyte-scale data warehouse, without any loss in performance and low cost: One of our existing customers stores more than 500 terabytes of data in an AWS Redshift database and the warehouse performance was good. We want to highlight that even if the warehouse size increases to petabytes, Redshift would still work fine and there wouldn’t be any performance issues and would cost less also.

This service can merge and integrate well with all databases. 

A feature I find most valuable in Amazon Redshift is that it's fast.

I also like that you can query the data lake using the Redshift spectrum.

You can build analytics on this solution, similar to QuickSight or Tableau, based on the Redshift Data API.

Amazon Redshift also has good integration and a good API. It's not hard to manage. It even has machine learning features.

Overall, I'm satisfied with the solution so far, and, from an accounting perspective, it works well for my tasks and duties. 

The product is very stable. 

Changing from local servers to the cloud is very easy. It's so nice not to have to worry about physical servers. 

Amazon Redshift has a very user-friendly SQL editor and it's very easy to use the connectors. The product provides a lot of options to connect to different sources and the modeling capabilities are also good. As a fully managed data warehouse solution, the two important aspects are the connectors and how well we can use SQL for data modeling. In both cases, this is an efficient product.

We like that the solution is a scalable, column-store database. We are completely talking about an analytic database.

The feature that we find most useful is the ability to do analytics on the fly. It's the most important because it gives us the ability to provide solutions to multiple customers.

• Easy to build out our snowflake design and load data
• Ability to dynamically increase space requirements
• Good speed
• Extremely reliable

Amazon Redshift is very fast. It has really good response times. It's very user-friendly.

I use Redshift Spectrum a lot, so that integration is good. Also, Amazon Redshift offers a relatively flexible structure, especially in terms of like, nodes that you can assign. Moreover, it is serverless, which is great.

The most valuable feature is its scalability.

I like the cost-benefit ratio, meaning that it is as easy to use as it is powerful and well-performing. There are only three parameters that you need to understand, which are the distribution key, the sort key, and the compression method or encoding method. Once you understand these, you can tune the performance.

The most valuable features are that it's easy to set up and easy to connect the many tools that connect to it.

The most valuable feature is the scalability, as it grows according to our needs.

The part that I like best is that you only pay for what you are using.

Scalability: Ability to load huge number of datasets (I have experience with petabytes of data) and process those things. Storage is not limited. We can increase whatever we want.

Performance: The distributed architecture of Redshift has the capacity to process the workflow in a different cluster and coordinate those things in the leader node, making the process much faster.

Flexibility: This feature is helpful for user to increase the node size and config depending on their need. There is no need to wait for hardware to be in place whenever we increase the dataset. Redshift provides the option to increase the node or cluster size whenever required.

Multi-formatted accessibility: The Redshift engine has the capability to read the following file formats: CSV, DELIMITER, FIXEDWIDTH, AVRO, JSON, BZIP2, GZIP, LZOP. The user can choose which is best for their requirements.

VPC configuration: VPC configuration secures our dataset, which we keep inside the Redshift cluster. This VPC config doesn’t allow any third party in or out bound against firewall.

Python UDF calls: This is useful for a user to create their own user-defined function through Python and import that class into Redshift and process the dataset.

• It is based on PostgreSQL.
• It’s managed. Meaning, AWS takes care of handling infrastructure, deployments, encryption, and uptime for you.
• It’s cheap when you consider the price per terrabyte per year.
• It’s integrated into the AWS stack.

Columnar storage technology is the most valuable feature of this solution.

The most valuable features of this product are:

• Processing huge data in petabytes
• Massively Parallel Processing (MPP)
• Concept of data compression
• The way it stores the data in drives especially with the distribution key
• Supports BI tools like MicroStrategy (MSTR) and Tableau
• Supports all the data warehouse core features such as SCD1 and SCD2, and different schemas like the star schema

The most valuable features of Amazon Redshift are that its fast and efficient. We have lots of TBs of data and it's very fast.

Redshift gives extremely fast response involving large tables. This is the most important feature I look for in data warehouse solutions. Often you came across use cases where it is not possible to distribute data on a certain column, yet you need this column in join conditions. Redshift stores data using a columnar approach, which is useful for data aggregation.

All this at an extremely low price makes it possible for small to medium sized organizations to use Redshift’s power to get business insights.

Redshift COPY command, because much of my work involved helping customers migrate large amounts of data into Redshift.

The features I find valuable in Redshift are JSON format support. You can copy JSON to the column and have it analyzed using simple functions. Second, is the parallel off/on where you can choose if you want it to unload to split files or into one file.

Column store and distributed processing is optimized for read access. We grew to 3000+ users with no impact.

Column store is a data compression technique for relational data. I’m using it now in SQL Server 2016. We configured a 16-core VM for handling requests on the DB. The recommendation was to separate inbound data packets into related chunks, which were 1/16th of the size.

This way, the import process could make full use of parallelization, and it worked. We imported 20 million rows of sales facts in less than 15 seconds, and the content was query-able immediately. I’ve never seen that before. This was impressive. This meant that we could completely rebuild the data warehouse to “current” from "scratch" within minutes, assuming that the data was in S3 already.

Tables that would typically be 2GB in size are now about 250MB. This means more data in memory. You can also configure the tables to live in the memory of all of the available cores. This is good for small dimension tables. You can also fragment them across all cores, for the larger fact tables. This allows for distributed query processing. Once you set it up, it just worked. It was all specified in the PG-SQL table statements.

There were two data centers in Sydney that were guaranteeing us a distributed solution. We really didn’t notice this. It was more of a check box situation. At one point, there was an outage at AWS, but it didn’t impact our operations directly.

In terms of valuable features, I like the columnar storage that Redshift provides. The storage is one of the key features that we're looking for. Also, the data updates and the latency between the data-refreshes are valuable.

The most valuable features to us are: speed, DML, the fact that it is cloud-based, the management console, and Boto3.

Because we are dealing with a lot of data, speed is always important. Redshift is blistering fast when doing "deep" copies and inserts. Conceptually, my data-transformation pipelines are a series of proprietary "waves" that leverage Redshift's DML/"deep" copy/insert strengths. Doing all this in the cloud allows us to easily test alternatives. We create different sized Redshift clusters and orchestrate them by using the SDK (Python Boto3). We go beyond the traditional DWH to "infrastructure-as-software".

• Fast load times 
• Flexibility in column definitions
• The ability to reload data multiple times at different times.

The valuable features are:

• PostgreSQL Interface
• Scalability
• Pricing/Maintenance/Setup

The PostgreSQL interface is good because you can play with big data with just SQL. This is one of the reasons why they made Hive.

However, Hive’s SQL is still not as standard as what Redshift provides:
http://docs.aws.amazon.com/red...

The processing of data is very fast.

The valuable features are performance, data compression, and scalability.

• Windows functions, such as LEAD and LAG.
• Allows you write complex queries and perform row by row processes.

Redshift's Excel features are handy. Redshift spectrum allows you to directly query the data on an Excel sheet. Now, SQL Server also allows this, but Redshift has many more features.

The best part about this solution is the cost.

The performance of this solution is good if you have only a few use cases and a few queries, but for larger applications, it is not so good. 

The solution's flexibility is its most valuable feature. It's also easy to scale and has relatively painless pricing.