Vertica BYOL, Red Hat
Micro Focus | 11.0.2-2Linux/Unix, Red Hat Enterprise Linux 7.5 - 64-bit Amazon Machine Image (AMI)
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Very easy to manage parallel processing.
What do you like best?
Data ingestion from various sources to provide key business insights like workforce metrics.
What do you dislike?
Something they don't support is enforcing foreign keys.
What problems are you solving with the product? What benefits have you realized?
Help to identify better insights and Analysis of market trends which helps to make better decisions for business.
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FAST AND SCALABLE
What do you like best?
The platform is fast. The feature set on the platform help for many use cases
What do you dislike?
I have not encountered any low lights on the platform.
What problems are you solving with the product? What benefits have you realized?
Vertica will help in unifying all the analytics required for an organization
Constantly improving And adapting
What do you like best?
One thing that stood out is the ease of use and having my data accessible and being able to integrate various tools to help visualize and analyze the data with ease.
What do you dislike?
I was bothered by the fact that I'm not able to migrate the data from one cluster to another as whole schemas instead of exporting the data into an S3 bucket using select statements and then importing that data into the destination cluster which caused multiple issues with some timestamp values in some tables.
What problems are you solving with the product? What benefits have you realized?
Vertica is playing a huge role in the collection, storage and analysis of data that's pouring in from multiple sources.
The best staging database
What do you like best?
Data reliability, scalability and transaction controls at its best. The compatibility with major ETL( Talend) and dashboarding tools(Qlik) is seemless. Just best!
What do you dislike?
Sometimes we might face node failures and failover controllers are not much handy. Data limit exceeds quickly and there are warnings in command line which i personally felt cumbersome.
What problems are you solving with the product? What benefits have you realized?
Vertica External tables have Good compatibility with Hadoop's hive. Data can easily be migrated and is quite fast for all datasets. Creating Reporting system in realtime and Near real time is really good.
Recommendations to others considering the product:
Vertica is a good substitute for enterprise database with all the supports and cross platform leverages.
All Rounder Platform for Data Analytics
What do you like best?
I like the tools and support which Vertica G2 provides. Most importantly I like it as helps in performing analytical operations very efficiently and effectively.
What do you dislike?
I would like them to improve the interface and increase more support with the cloud platformd such as AWS, AZURE and GCP. Also like to see if Vertica G2 can integrate more tools and programming features.
What problems are you solving with the product? What benefits have you realized?
The major problem is the optimisation and performance tunning which I think Vertica G2 can handle very efficiently. Processing and analysing the non structured data is a major problem I see which Vertica can do it.
Good analytics platform
What do you like best?
Ease of use, client centricity, and the customer support
What do you dislike?
This product is still in naive form and needs some improvement
What problems are you solving with the product? What benefits have you realized?
Managing big data and doing analysis for business purposes
Vertica makes analytics easier
What do you like best?
You can find vertica in almost every popular cloud services. Amazon web service, Google cloud platform and microsoft azure.
What do you dislike?
Vertica optimizer for query handling is not as great as other leaders. You need to know ahead of projection and sql query then only it makes sense to use it
What problems are you solving with the product? What benefits have you realized?
Doing EDA and gap analysis in vertica is fairly straightforward.the best thing is that we dont require to transfer data from one data warehouse to vertica to use it. Hdfs support is great. One tip is to use vertica in two different modes for project management . You can always makes use of both storage and compute engine using a single mode also.
Recommendations to others considering the product:
You can use it without the need to data transfer from your traditional data warehouse
Great lab environment
What do you like best?
Exercises were clear and informative, nice to have a possibility to restart modules if needed. Nice to have the 3 node cluster with vertica management console installed.
What do you dislike?
This is a tricky question. I didn't like that in the test, I couldn't see in the summary at the end what questions were wrong because I thought I had everything right (93%). Also, some questions were a bit misleading - for example I think the question was how Vertica stores the data: answer: in projections, but you could argue that in tables as well. I don't remember the question exactly.
What problems are you solving with the product? What benefits have you realized?
Nothing for now, but I will be assigned to a project where we will be working with a Vertica cluster.
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The best integrated multitool for data storage and analytics
What do you like best?
What you usually expect from a storage solution is that it can save and provide comfortable access to all the data, not only for engineers but for plain users.
My experience with Vertica shows that it is a versatile and resilient tool, which provides a lot of fine-tuning knobs, but at the same time forgives the mistakes you make on data architecture design steps and at the same time motivates to incrementally enhance your solutions. A lot of integration capabilities, including Kafka, Spark and Hadoop, powerful security features.
It is also a software solution that makes it easy to scale up much easier than in appliance solutions.
My experience with Vertica shows that it is a versatile and resilient tool, which provides a lot of fine-tuning knobs, but at the same time forgives the mistakes you make on data architecture design steps and at the same time motivates to incrementally enhance your solutions. A lot of integration capabilities, including Kafka, Spark and Hadoop, powerful security features.
It is also a software solution that makes it easy to scale up much easier than in appliance solutions.
What do you dislike?
Several features are not (yet) presented in Vertica, which might be useful:
1. Procedures - not to process data or perform analytics, but to enhance SQL with DSL or common data access routines. It is particularly useful for DWH, when highly normalized layers require to have dynamically generated SQL to access.
2. Conditional execution. It will be handy for schemata management. With conditional execution, it is possible to define schema with idempotent change management.
3. Federative data access - sometimes it is much easier to just ask something in other databasess in real time, than to integrate it. Particularly useful for analytics.
Hope it will ve delivered some day.
1. Procedures - not to process data or perform analytics, but to enhance SQL with DSL or common data access routines. It is particularly useful for DWH, when highly normalized layers require to have dynamically generated SQL to access.
2. Conditional execution. It will be handy for schemata management. With conditional execution, it is possible to define schema with idempotent change management.
3. Federative data access - sometimes it is much easier to just ask something in other databasess in real time, than to integrate it. Particularly useful for analytics.
Hope it will ve delivered some day.
What problems are you solving with the product? What benefits have you realized?
Vertica is our primary tool for big data analytics (for structured data). Here's what we do:
- Ingest data from Kafka directly, and from other sources using Apache Nifi and Apache Airflow
- Create data pipelines for building cleaned and trustworthy datasets on user marts level
- Bulding detailed layer using modified anchor modeling approach
- Manage access to data via global role-model and unified data access policies
- Perform retention operations for old data partitions by moving it (again directly) to Hadoop
- Business analytics via Tableau
- ML models training and scoring, ad-hoc analytics and sandboxes
- CI/CD for schemata changes and versioning (using Gitlab)
All in all it is a tool that has a lot of applications in data management field.
And in most if the application cases it is not just "has capability", but does it on state-of-the-art level.
- Ingest data from Kafka directly, and from other sources using Apache Nifi and Apache Airflow
- Create data pipelines for building cleaned and trustworthy datasets on user marts level
- Bulding detailed layer using modified anchor modeling approach
- Manage access to data via global role-model and unified data access policies
- Perform retention operations for old data partitions by moving it (again directly) to Hadoop
- Business analytics via Tableau
- ML models training and scoring, ad-hoc analytics and sandboxes
- CI/CD for schemata changes and versioning (using Gitlab)
All in all it is a tool that has a lot of applications in data management field.
And in most if the application cases it is not just "has capability", but does it on state-of-the-art level.
Long-term leader for big data analytics
What do you like best?
I most like the breadth of functionality that Vertica offers. For example, analytic lags and deltas and event pattern matching.
What do you dislike?
Whilst the documentation is generally good, I do manage to find and report back mistakes. It can sometimes take a while for these corrections to be incorporated into another documentation release.
What problems are you solving with the product? What benefits have you realized?
Log analysis and aggregation, complex KPI generation, and sessionisation of data. Using Vertica we have managed to combine data from a large number of sources and query it in one place.
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