Organizations challenged with continuous data movement and transformation at scale can streamline complicated loading processes for complex data types and eliminate the need for standalone tools like Spark and Informatica that add latency and overhead to hyperscale workloads. Native support for ETL and ELT workloads – Version 22 of the OHDW delivers improved performance for loading, streaming, and extract, load, transform (ELT) workloads.

“With version 22, Ocient is enabling customers with the capabilities they need to increase price performance, consolidate multiple resource-intensive tools into a single solution, and unlock new opportunities for product development and real-time decision intelligence to help grow their business.” Marketing Technology News: Martech Interview With Mike Nash, President, KS&R Ocient’s ability to continuously stream, load, and transform large scale datasets and execute machine learning directly within the database enables customers to execute complex, continuous data integration, transformation, and exploration alongside real-time analytics and OLAP-style workloads from a single solution stack. This latest release builds upon Ocient’s hyperscale data integration and data movement capabilities while adding new real-time analytics features, query performance enhancements, and support for additional business intelligence (BI) tools. Ocient, the leading hyperscale data analytics solutions company, announced the version 22 release of its flagship product, the Ocient Hyperscale Data Warehouse (OHDW). I would better prefer Microsoft Excel over Metabase since Excel also has the ability to integrate with live data for daily reports.Version 22 provides customers enhanced capabilities built to eliminate disparate data integration and transformation tools and enable data and analytics consolidation at scale Overall, if I were to create visualisations through Metabase, I will be bound by the limits of the tool. I have added you to the email list, and therefore, you should be receiving the daily report everyday at 8 pm PST. I cannot add custom labels, I cannot change colors.ĥ) There is no way to share dashboards other than through an email. I am sure that I have joined the tables in a similar manner as I did in Tableau.Ĥ) No formatting flexibility. It is impossible for me to filter the duplicates through metabase. I can see that there are a lot of duplicates in the dashboard that I have created. Creating filters is not great with this tool.Ģ) The tool has a lot less tutorials and community support.ģ) Highly reliant on SQL.

I have to create separate visualizations for each of: yesterday, last 90 days, last 60 days, last 30 days. For example, it gives suggestions for calculating mean of a particular column, say, order amount by customer, and the tool does it directly for you.Į) If the data is fed correctly through database, visualizations can be created simply by drag and drop.ġ) The tool is quite inflexible: I wanted to create one filter that can filter data based on orders in last x days. The tool provides certain suggestions such as aggregates on which visualizations can be developed. ġ) It is fairly simple to use, and learn, compared to TableauĪ) you can mail reports or dashboards daily / monthly/ or after any specified time period directly to an email list.ī) It is very easy to join tables on keys.Ĭ) It is EASIER to drag and drop tables/ visualizations onto dashboards compared to Tableau.ĭ) It is quite easy for a beginner with no experience in building dashboards- to create visualizations. As a use case, I tried to recreate daily reports, that have already been published through Tableau. Over the past week, I had an opportunity to study Metabase quite extensively.

Metabase vs power bi