Amazon Redshift

Amazon Redshift

Score 8.2 out of 10
Amazon Redshift

Overview

What is Amazon Redshift?

Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.
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Recent Reviews

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Pricing

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Redshift Managed Storage

$0.24

Cloud
per GB per month

Current Generation

$0.25 - $13.04

Cloud
per hour

Previous Generation

$0.25 - $4.08

Cloud
per hour

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting / Integration Services
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Product Details

What is Amazon Redshift?

Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.

Amazon Redshift Technical Details

Deployment TypesSoftware as a Service (SaaS), Cloud, or Web-Based
Operating SystemsUnspecified
Mobile ApplicationNo

Frequently Asked Questions

Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.

Reviewers rate Usability highest, with a score of 10.

The most common users of Amazon Redshift are from Mid-sized Companies (51-1,000 employees).
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Comparisons

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Reviews

(1-25 of 38)
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Score 9 out of 10
Vetted Review
Verified User
We are using Amazon Redshift as our main data warehouse to store most of our data, the whole process consists in extract the data from different sources, we do some transformations when needed and the data is finally stored in Amazon Redshift in order to be used afterward by one of our Business Intelligence tools.
  • Easy setup (if you are on AWS Cloud Environment, just few clicks)
  • Easy learn (Good documentation)
  • Speed
  • It could bring some more features like we do have in Snowflake (Mainly the UI)
If you are looking for a data warehouse where you don't need to worry about maintenance and scalability, Amazon Redshift should be one of your options once it is a self-managed data warehouse with many connectors and easy usage as well. Besides that, if your environment runs on AWS, it is even easier to integrate.
Score 8 out of 10
Vetted Review
Verified User
We use Amazon Redshift for structured data warehousing. It allows us to store, retrieve, and analyze large volumes of structured data quickly and efficiently. It is used to support decision-making, identify trends, and gain insights into the business. Furthermore, we use Amazon Redshift can be used to create dashboards, generate reports, and perform ad-hoc queries on data to support business intelligence and analytics efforts. We also use it to support our customer service applications or fraud detection systems
  • Data warehousing
  • Business intelligence
  • Data insights
  • Cost can be prohibitive
  • User interface could be more intuitive
Amazon Redshift is well-suited for a variety of scenarios where businesses need to store, retrieve, and analyze large volumes of structured data. Some specific scenarios where Amazon Redshift may be well-suited include: Data Warehousing, Business Intelligence, Data Migration as well as Real-Time Data Processing. On the other hand, Amazon Redshift may not be the most appropriate for unstructured data, organizations with low volume of data or Real-Time Stream Processing.
Dileep Kumar | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
I use it as the data warehouse of our clients. I use it to build data transformations of user activity logs to ML features. I use the sql workbench to explore datasets and understand data schemas. Post that, I generally connect to the warehouse either through dbt or from jupyter notebooks.
  • Seamlessly integrates with the data in s3
  • Workbench provides useful way to query the tables within aws console
  • Postgres flavor of sql gives powerful capabilities such as window functions
  • Json support in sql is very limited.
  • Array type columns are missing. They are by default converted to strings
  • Sql workbench often goes unresponsive. I have to reload for the queries to run
  • A search option in the sql workbench would be great, which let's users search the whole db for a match on columns, tables etc
It is a solid data warehouse on top of the AWS ecosystem. If most of your infra is on AWS, it makes good sense to go for it. But it is expected to be tuned well by a data engineer for an optimal performance. For a data scientist too, the SQL is a bit limited when it comes to unstructured columns in the tables. Arrays, jsons, etc have very poor support compared to other warehouses.
Ravi Khunt | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
I am using Amazon Redshift in our organization to deal with SQL operations by using a live web editor. Earlier, we need to install separate software or write queries and call APIs to get data from the database, but Amazon Redshift provides online tools to work with databases same as working locally.
  • Serverless architecture.
  • Data Analytics.
  • Provides SQL Web Editor.
  • Data sharing.
  • Auto scale data warehouse.
  • Can improve data sharing feature.
  • Improve data analytics service.
  • More customizations and charts in Query editor.
Amazon Redshift is well suited when you don't want to manage your data and storage of database because Amazon Redshift is doing really well by itself. Auto-scaling will look into your cluster data warehouse size and automatically scale when more space is needed. If you want full control of your data storage locations, like the need to keep data in your organization only and not in the cloud, then it is not for your system.
Sameera Srivastava | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Amazon Redshift is primarily used as a data management solution by Product Analytics Group. We currently have various sources of capturing data like Heap, Delighted, Salesforce and it is convenient to build an ETL from these sources to Redshift. This enables us to merge all these data sources into single view in a BI tool like Power BI
  • Ease of setting up ETL
  • Uploading data into Redshift via AWS
  • Querying is quick
  • Missing option to restrict duplicate records
  • Lacks complex data sets like udf
  • Does not offer UI based querying & visualisation option like Looker
It is well suited in scenarios where you have distributed data sources and would like to build an ETL pipeline with limited data engineering efforts. Operations time and cost is relatively low compared to other tools. Also it offers great connectivity with Heap with no technical know-how required. It is mostly self managed and reliable.
I am really amazed by the level of documentation done by AWS on most frequent topics. Rarely do I have to look up for an answer elsewhere. Also they offer 24X7 business support which is quick and easy to get.
Score 10 out of 10
Vetted Review
Verified User
Amazon Redshift is our Data Warehouse, where we store our processed data (Hot data) for various initiatives like BI, Analytics, DataScience, etc

We also use Amazon Redshift Spectrum as our Data Lake, where we store raw (un-processed) data (Cold data) for historical analysis, trends, etc

We store various standard data in Redshift like:
Bronze (ETL-ed data),
Silver (Materialized Views data), and
Gold (Rollups/Aggregated/Dashboard-ready data) in [Amazon] Redshift





  • [Amazon] Redshift has Distribution Keys. If you correctly define them on your tables, it improves Query performance. For instance, we can define Mapping/Meta-data tables with Distribution-All Key, so that it gets replicated across all the nodes, for fast joins and fast query results.
  • [Amazon] Redshift has Sort Keys. If you correctly define them on your tables along with above Distribution Keys, it further improves your Query performance. It also has Composite Sort Keys and Interleaved Sort Keys, to support various use cases
  • [Amazon] Redshift is forked out of PostgreSQL DB, and then AWS added "MPP" (Massively Parallel Processing) and "Column Oriented" concepts to it, to make it a powerful data store.
  • [Amazon] Redshift has "Analyze" operation that could be performed on tables, which will update the stats of the table in leader node. This is sort of a ledger about which data is stored in which node and which partition with in a node. Up to date stats improves Query performance.
  • Amazon Redshift is a Managed Service. But it is Not a 100% managed service. We still need to configure it with WLM (Work Load Management) settings, and add Query Queues to make sure it's resources aren't wasted and it is performant at it's best state, all the time
  • [Amazon] Redshift has a concept of "Vacuum", which is an operation to claim the disk space back from deleted data/tables. They recently started doing automated vacuuming. Prior to that we had to do that at regular intervals, to claim the data back.
[Amazon] Redshift is suited for various use cases like Time series data, Structured / relational data, Semi structured data like JSON, etc.

[Amazon] Redshift might not work 100% well with full performance, for Graph DB use cases.
Prashast Vaish | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
I am working with an insurance client where a lot of claims and policy data comes in every day, we use amazon redshift to perform ETL and analyze the data to gain business insights.
Amazon redshift is an integral part of our data analysis.
  • Amazon redshift is super quick due to its Massive parallel processing
  • Amazon Redshift is compatible with many visualization tools which helps visualize the insights
  • Amazon redshift has almost 0 downtime and allows for a massive store of data
  • Since Redshift is a part of a larger AWS ecosystem, connecting with other resources is never a problem
  • Amazon redshift could have more detailed documentation including practical examples
  • Amazon redshift still lacks some of the advanced concepts which are possible with MS-SQL and others
  • It should have a feature where users can visualize the data stored for a better understanding
Amazon redshift is best suited for data analysis and is not suited for transactions.
for eg. you can use amazon redshift to gain insights from a large data set but cannot use it to do a transaction level update and insert
Score 10 out of 10
Vetted Review
Verified User
We have decided to purchase Amazon Redshift since we started the project of building a new "data lake," so the first step was to decide which tool would be more appropriate to use as a data warehouse. Since we have everything on the cloud, we choose Amazon Redshift to connect our current tools on AWS and integrate the data.
  • Data integration is very simple to perform
  • The tool provides some advice that is very useful
  • Their support is always complete and easygoing
  • Their documentation could be even better
If you are looking for something easy to implement that will give you a nice performance, I would suggest Amazon Redshift. I'm using it in AWS environment, so I don't know if in another cloud environment the performance and all the features would be nice as well. It's also important to check if the price fits to you too.
Arthur Zubarev | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Amazon Redshift is a PostgreSQL based solution was seen as a drop-in replacement for several Postgres based databases (or schemas in Postgres parlance).
The eventual product: a Bill Inmon principles-based Data Warehouse served as a point or source of a single truth. It aided in decision making, historical outlooks and forecasting across various organizational verticals - the Finance, Marketing, and Medical Research. It was also possible to deliver data extracts to 3rd parties or visualize data on demand.
  • Data retrieval experience really gets improved.
  • In terms of database management, it is really a no management at all in AWS. There is no even an OS to take care or worry about.
  • Auto or on-demand scaling is nice.
  • Integrates quite well with other products within the AWS ecosystem.
  • The number of connections is too small, I think at around 50 are allowed in parallel. With some ETL and apps connecting all the time, this brings an undesired possibility to some users or tools being unable to connect.
  • Needs some tuning.
  • The logging part is almost nonexistent.
  • Can be quite costly in the long run as opposed to just RDS or on-prem/dedicated solutions.
If the number of connections is expected to be low, but the amounts of data are large or projected to grow it is a good solutions especially if there is previous exposure to PostgreSQL. Speaking of Postgres, Redshift is based on several versions old releases of PostgreSQL so the developers would not be able to take advantage of some of the newer SQL language features. The queries need some fine-tuning still, indexing is not provided, but playing with sorting keys becomes necessary. Lastly, there is no notion of the Primary Key in Redshift so the business must be prepared to explain why duplication occurred (must be vigilant for).
Redshift support is seamless. The system is MPP (distributed), so it is highly available, always backed up by AWS and you can also have read-only replicas (at a cost) which help overcome the number of connections issue.
Although, AWS looks like is not going to upgrade its storage engine to the newer version of Postgres which is a big pity.
Score 9 out of 10
Vetted Review
Verified User
We use Amazon Redshift for our insights platform in our R&D space. Our team creates reports and dashboards on tools for business use. Amazon Redshift provides greater supply chain visibility, increased information on product movement, and high efficiency at a much faster rate.
  • Robust as compared to traditional database/data warehouse
  • Offers significant query speeds
  • Low cost of ownership
  • Provides MPP only for AWS-supported storages
  • Prerequisites for configuring tables are not easy
  • Not great for use with web apps
Amazon Redshift performs extremely well for reporting/analytics data and is way ahead of other competitors. The biggest challenge is migrating data from on-premises databases to Amazon Redshift. The initial hurdle is a major one.
January 15, 2021

Redshift trumped Hive

Score 9 out of 10
Vetted Review
Verified User
It is used within a few departments. It is used to solve certain legacy problems that have not yet been ported over to other more suitable approaches.
  • Large scale SQL
  • Standard SQL
  • Handline full text queries
  • Sampling
  • Bonafide indexes
  • Provide query interface that can store queries and run long-running queries, then notify the user
It's appropriate for ad hoc queries on semiorganized data.
Bojan Sovilj | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Reseller
Cloudwalker offers analytic services for the gambling industry. The gambling industry has vast amounts of data that are high speed and variability. Our services from Redshift help gambling companies have better control of their bookmaking product, have a complete view of customers betting history, helps with detecting problematic accounts, etc.
  • Redshift has concurrency scaling helps serve more customers queries
  • Redshift has automatic table compression having less disc space consumed comparing to other data warehouse solutions
  • With ra3 new node types we can separate storage and compute
  • Having automatic vacuum delete helps having conzisent performance in cases where data variability in dwh production zone is present
  • Consistent service improvements from AWS: temporary tables, null handling in joins, single row inserts, materialized views
  • Frequent changes of management console look and feel
  • Automatic vacuum sort doesn't work for several billion rows tables
  • Disc IOPS performance monitoring excluded
Redshift is great data warehouse solution if you have several billion rows tables. More than 200 very important improvements were added in several years' time. With new Redshift instance types solution has separation of storage and compute and magnitude better query response times. Don't use Redshift if you have less than several billion rows tables.
Several service improvements saved us literally!
December 30, 2020

Redshift Review for you!

Duncan Hernandez | TrustRadius Reviewer
Score 6 out of 10
Vetted Review
Verified User
Amazon Redshift is being used by our whole organization. It is our primary tool for our data warehouse. We decided to switch to a cloud database because our in house servers just weren't able to keep up with our need for fast data delivery. We can adjust the speed up to where we need it to be and it has been very useful.
  • Aggregation
  • Extracting data
  • postgres Based
  • Could be faster
  • Limited sql workbenches
  • Expensive when speeding up the processing
Redshift is best suited for our data storage and designing our fact and dimension tables. We keep our non-structured data there that can be accessed at any time as well as our relational database. I'd say that if you don't have a need for a relational databasae, then Redshift probably isn't going to be a viable product.
December 30, 2020

Great Data Lake Solution

Score 9 out of 10
Vetted Review
Verified User
Amazon Redshift is being used by some departments of our company to bring solution to data warehouse. Our data center was not large and scalable enough to have big scale data warehouse. So we take Amazon Redshift as one of the data warehouses we use to help us in data transformation.
  • It is very powerful, can hold anything you have.
  • It is scalable. Small or big, it can help you.
  • It is very fast. Can spin up cluster in minutes.
  • As data warehouse, it does not support fast I/O.
  • Learning can take more than expected.
  • You are not managing your data 100%.
If you do not require a fast I/O rate. And frankly you should not expect that from data warehouse. And if you do not mind that your data is on cloud and only on AWS, and if you want a scalable and fast implementation of data lake, then you should consider Amazon Redshift.
Jay Padhya | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
We are performing a POC (proof of concept) across multiple cloud vendors, and we have evaluated GCP, Azure, and Amazon. We plan to go on-site cloud from on-premises, and we will evaluate all databases across all cloud providers. We are making the current database over Amazon Redshift, if it can help us do the exact same job as we have on-premises SQL Server. The on-prem option is good for replication and security, and we are evaluating how good it can be on ML application support.
  • Robust
  • Great UI
  • Price
  • Implementation in non AWS server
Except for the price, Amazon Redshift is a great tool and has the fastest performance across all the data warehouses we have seen. It's easy to connect with Talend, which makes it a better option to use. I like the UI better than most of the other DW. Overall it's a great DW tool.
Good look; good UI
High price
Score 9 out of 10
Vetted Review
Verified User
My team is handling all the marking activities for all different product verticles for our company. We get a lot of streaming data that we focus on marketing. We also store this data carry out annual audits and aggregations to analyze our marketing progress. Amazon Redshift helped us with this, and it was a breeze enabling it in our environment as we were already on AWS.
  • Replication is excellent, we did not have to worry about reliability.
  • Their auto-scaling feature came to our rescue when it came to cost management.
  • It became expensive over time as the data increased over time.
  • It could not separate users from using the same infrastructure.
  • The initial costs were very low, and it was super easy for us to spin up databases. Also, setting up multiple instances were easily managed with just a click of a button.
  • The costs increased gradually, and it became more expensive than planned. The query analyzer was not up to the mark and needed constant support from the AWS support team. Gladly they were willing to help and had a good experience with them.
The support was great and helped us in a timely fashion. We did use a lot of online forums as well, but the official documentation was an ongoing one, and it did take more time for us to look through it. We would have probably chosen a competitor product had it not been for the great support
November 30, 2019

Redshift Review

Score 2 out of 10
Vetted Review
Verified User
Amazon Redshift was our enterprise data warehouse as a backend to our BI solutions.
  • Fixed cost.
  • Tunable table design.
  • Need to provision warehouse for highest capacity.
  • No real separation between computing and storage (even when considering Spectrum).
  • All users share the same infrastructure resulting in frequent 100% utilization error messages.
  • A leader node can become a bottleneck for too many concurrent aggregate queries.
Redshift is appropriate when the number of concurrent users are low and pointed queries are the focus. It is not appropriate when a large number of concurrent users is to be supported,
We had premium AWS support so can't speak about support for those who don't sign up for it.
September 27, 2019

Redshift is way too easy!

Score 9 out of 10
Vetted Review
Verified User
Redshift is our data warehouse used by our organization. It takes data from different sources and put them together in Redshift for our Analytics team to diagnose.
  • Since it's part of AWS it is fairly quick and easy to set up.
  • You can add nodes fairly quick to expand the data needs.
  • Performance from the analytics reports accessing Redshift is really good.
  • Better database management when looking up table metadata or sizes of tables.
  • Need a better query analyzer.
  • Finding errors during a data load can be a little daunting at times.
It's very cost-effective from other databases we were using for our data warehouse. It was really easy to set up and it used our ETL tools to migrate data from different data sources. We added functionality add aggregate the data set for our Analytics team to analyze.
A lot of times when we have issues in Redshift, we have to google issues that may have come up in the past. We have not contacted AWS directly for any issues.
Akshaya Bhardwaj | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Amazon RedShift is used in all departments and accounts. This tool is newly integrated into the system so as to work with the data on the cloud. There are various projects which are moving from SQL servers to ARS because of its capacity of working and managing the data in the cloud.
  • We can connect with multiple servers and can fetch the data easily from one server to other.
  • It supports the syntax of the bots of the SQL servers, MS SQL and Oracle SQl. This makes it pretty handy to use.
  • Here we use views instead of tables, so we can clearly see the flow of data.
  • It works very slow in the cloud environment.
  • No statistical inbuilt functions are available within the tool.
  • Its user interface is not very attractive.
  • Often it goes into deadlock state, which kills the running jobs.
Amazon Redshift is well suited for fetching data from multiple different sources and servers, and it is very easy to learn how to use this tool. It is less appropriate for situations where you may need to process only limited rows, as this takes a large amount of time since you cannot create multiple tables in it.
Score 7 out of 10
Vetted Review
Verified User
AWS Redshift is the cloud-based data warehouse where we store our application level datasets and is used further for insights from the stored dataset. It improves the decision support for our business based on data analytics on a large set of real-time datasets which force the business processes to the next level. It provides good performance with high availability for essential data analysis and valuable intelligence.
  • Easy query and fast execution
  • High performance and availability
  • Support of large datasets
  • Scalable solution
  • Database optimization
  • Time consuming process for schema design and modification
  • Integration is little bit difficult
Amazon Redshift is the data warehouse under the umbrella of AWS services, so if your application is functioning under the AWS, Redshift is the best solution for this. For large amounts of data, the application is the best fit for real-time insight from the data and added decision capability for growing businesses. If your application is outside of AWS it might add more time in data management.
Vibhakar Prasad | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
1)ETL(Talend) data from source applications (Salesforce, Jira, OpenAir, NetSuite, Sharepoint, Active Directory, Office 365, etc.) to S3 bucket and from S3 bucket push data to Redshift (sync time is 10 minutes intervals. Data is available almost real-time only lagging 10 minutes).
2) All Department use it—Engineering, Sales, and Marketing.
3) As I said data is almost in real-time, so it is very useful for taking real-time decisions for upper management. We also reduced Salesforce licenses, because most of the users only used it to see reports. Now they are happy to used Redshift.
  • We reduced the number of Salesforce licenses— Engineering, Sales and Marketing guys are happy to query data from Redshift.
  • Very fast to provide a huge data set with complicated measure.
  • Some of the calculations failed in Salesforce. Redshift returns with the same calculations very fast.
  • Very easy to maintain, no need to worry about hardware failure.
  • We are not able to modify column size.
I recommend all to use Redshift, It is easy to use and maintain. We have reduced the number of Salesforce licenses due to real-time data we have in Redshift. People are happy to use Redshift.
Score 8 out of 10
Vetted Review
Verified User
Redshift is being used by engineering for our data warehouse or data lake, if you will. It's part of our ETL pipeline, where the data is used to form dashboards and analytical queries across all of our initially segregated data. So it is kind of a source of truth linking data across the company. These dashboards are accessible across other departments in the organization. The data is consumed by everyone, not just engineers.
  • It's fast for data analytics across multiple columns.
  • Essentially, it's good for big datasets.
  • By using RedShift you're kind of married to using AWS's other services, e.g. Redash.
  • You need your data in the cloud.
  • No separate storage and computing.
  • No structured data types.
  • Doesn't scale easily.
Use Redshift for data warehouses, especially if your data is already in the cloud (AWS). It's great for large datasets, and fast too, even if your dataset is column heavy. It's less so for when you have a bunch of rows. All in all, it's a good starting point for any aspiring data warehouse, but there are other promising solutions too. E.g. Snowflake.
Score 10 out of 10
Vetted Review
Verified User
Amazon Redshift is being used as our primary analytic data-warehouse. This allows our data and analytic team to build report and query data without going directly to our production database. It is a central data repository from external data sources as well, data we import from 3rd parties and segment.
  • Complex queries
  • Aggregation
  • Fully managed service
  • Works very well with most BI/reporting solutions
  • Stored procedures
  • Job scheduling
  • A easier way (perhaps a GUI) to manage users permission
Amazon Redshift is great for analytics, reporting, and complex queries for statistical modeling and machine learning. Its ability to run parallel queries in a simple SQL environment makes the transition from traditional DB very easy. Very good for loading/reading/writing large datasets. I would not recommend RedShift for an environment that requires single row reads/updates, which it is not optimized for.
Jacob Biguvu | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Capital One has many LOBs (line of businesses). I have supported IAM and Commercial LOB. They are using Redshift as a data warehouse solution. Oracle is not a Data Warehouse solution but was being used when the application was on on-premises. When they wanted to migrate all data to the Cloud, they chose Redshift as a solution to move the data from Oracle. Oracle is not a data warehouse solution. Redshift has been found as a good solution because of its unique features such as its MPP architecture, columnar architecture, and storage capacity.
  • User-experience. The user wants something quick to view the output, rather than spending too much time
  • preparing a code prior to seeing the output. Redshift provides SQL type queries. This makes any user happy and comfortable.
  • Architecture is very straightforward and simple to understand, such as MPP architecture, Encryption, and Columnar database design. We can easily address issues and help others to understand.
  • Scalability. We can scale-up and scale-down based on our workloads.
  • Performance tuning and database optimization can be done using the system tables and advisors. These solutions are similar to the solution available for Oracle SQL Server. It makes it easy to do the optimization for queries and databases.
  • The concurrency and scale up based on it could be improved. It would be good if it scale-up and scale-down the memory/CPU capacity automatically based on workload.
  • Often we experience slow on queries and dashboards. Self-tuning option in WLM does help.
  • Optimizing the areas such as Vacuum and reorganize the column data (sorting over time) automatically.
Amazon Redshift is good for when you need a Data Warehouse solution or a user-experience such as SQL kind queries. It is also good if you have limited budget constraints. It's not suggested when someone has DML queries such as INSERT, UPDATE, DELETE.
Score 9 out of 10
Vetted Review
Verified User
We use it as our data warehouse for reporting purposes.

My company used it solely for reducing the performance overhead of running long SQL queries. The seamless implementation of Redshift allowed us to get the data ready to go for our customers to run the reports they need. It is currently used by a few customers, but we are trying to get each of our customers to use this rather than using the traditional OLTP database.
  • Easy to work with
  • Seamless implementation with matillion
  • Massive data reads and inserts
  • I didn't like the security aspect of this where it asks us to create views for each customer.
  • It does not support row-level controls.
  • Some SQL queries are faster on native SQL than here. But it could be the data conversions that is causing it.
[It's] very good for reports and dashboards and is easy to use and fast performance makes it a good choice. It excels in columnar architecture and aggregates. If you have many clients as we do, you need to have separate schemas for each of them. That is a good way but also there is too much clutter spread all over. That is the only drawback I see, end users can see data in the schema, there are no individual permissions allotted.
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