UNNEST vs EXPLODE Functions
Vertica | Vertica Blog
by sruthi
4M ago
Vertica provides two functions, UNNEST and EXPLODE to expand arrays into one or more rows. These functions offer the same functionality with subtle differences in syntax and output. Let’s understand with a simple example. CREATE TABLE orders ( orderkey VARCHAR, custkey INT, prodkey ARRAY[VARCHAR], orderprices ARRAY [DECIMAL (12,2)], email_addrs ARRAY[VARCHAR]); eonv2330=> select * from orders. orderkey | custkey | prodkey | orderprices | email_addrs ..read more
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Unveiling the Most Recent Version of the Vertica Grafana Data Source Plugin
Vertica | Vertica Blog
by Amrita
5M ago
With over 380K downloads, the Vertica Grafana Data Source plugin just got an upgrade! The plugin was migrated from the deprecated older Grafana toolkit to align with Grafana’s new Create-Plugin tool. This accelerates the plugin development with their modern build set up that requires no additional configuration. Additionally, the Vertica SQL Go driver received an upgrade, making the leap from version 1.3.1 to 1.3.3. Excitingly, the plugin is now fully compatible with both Apple M1 and M2 processors, further broadening its usability and appeal. Check out the most up-to-date version on the Vert ..read more
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Checklist for Inconsistent Execution Time of Same Query in Eon Mode
Vertica | Vertica Blog
by sruthi
5M ago
In Eon Mode, when a query is executed and if the ROS containers associated to the tables in the query are not present in depot, it will fetch results from the communal storage bucket. In the next immediate run, the query should provide results to the user from the files present in the depot. However, there are scenarios where high count of queries against various tables run in parallel and depot is flushed to make space for new ROS containers needed to serve other queries. This is totally acceptable. What should you do when you observe inconsistent execution times for multiple runs of the sam ..read more
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Exploring VerticaPyLab: A Quick Start to ML, Data Analytics, and Vertica
Vertica | Vertica Blog
by Umar Farooq
5M ago
Authored by Badr Ouali and Umar Farooq Ghumman Welcome to VerticaPyLab, a transformative solution that paves the way for effortless Machine Learning and Data Analytics. If the world of Python’s ML libraries has intrigued you but appeared complex to navigate, VerticaPyLab is here to redefine your journey. It is designed to make ML accessible and streamlined, regardless of your expertise level. What is VerticaPyLab? VerticaPyLab comprises two containers: one housing the robust Vertica analytical database and another featuring JupyterLab, an interactive computing environment. These two componen ..read more
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How to Group Timeseries Data at Any Granularity
Vertica | Vertica Blog
by Marco Gessner
7M ago
You might have come across this. You would have obtained a huge set of time stamped log data or sensor data that you would like to understand. Millions of rows are nothing for human consumption – and far too much for plotting on a monitor that just has a few thousand pixels across – why fetch millions of rows across a busy network, when you can just plot a few thousand of them? What could help you is a minimum/average/maximum value for a time slice. All DBMS-s I know can truncate a timestamp value to the second, minute, hour or day, to “snap” a timestamp to the previous or next full unit of t ..read more
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How to Use UDx to Extend Vertica Live Aggregate Projections
Vertica | Vertica Blog
by Maurizio Felici
7M ago
Thanks to its sophisticated optimizer and extremely efficient query engine Vertica can process data aggregations order of magnitudes faster than traditional Database Management Systems. To further boost data aggregation performance, we can use Vertica’s Live Aggregate Projections (LAP from now on). The concept is very simple: pre-aggregate data once during load operations rather than doing it again and again at query run time. So, for example, if we load 1,000,000 values in in batches of 1,000 INSERTs (each consisting of 1,000 elements), using LAPs, we will have to aggregate only one thousand ..read more
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Vertica’s Server-Based Replication
Vertica | Vertica Blog
by Moshe Goldberg
7M ago
Empowering Seamless Data Copying in Vertica Eon Mode Overview In our latest installment of exploring the untapped potential of Vertica, we delve into the exciting new feature introduced in version 23.3 – Server-Based Replication. This cutting-edge functionality allows users to efficiently copy data from one Eon Mode database to another, revolutionizing the way data replication is handled in Vertica. With server-based replication, data is directly copied from the source database’s communal storage location to the target database’s communal storage location. This streamlined process eliminates ..read more
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How To Filter a Behavioral Pattern in a Time Series
Vertica | Vertica Blog
by Marco Gessner
8M ago
Behavioral patterns in time series are something that many analysts are keen to find in time series. A clickstream analyst wants to find the series of clicks that happened between coming to the website, browsing one or more articles in the web site, and finally filling the basket and checking out; a financial analyst wants to find the v-shapes in the curves of stock values, consisting of two or more rows where the stock value went down, followed by at least one row where the stock value went up again. The peculiarity of this data search need consists in the words “one or more”. It means that ..read more
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Unlock the Potential of Machine Learning in Vertica with Saagie: Automating your ML Pipeline
Vertica | Vertica Blog
by Amrita
9M ago
Accelerate your data project pipelines with Saagie using Vertica’s in-database machine learning capabilities. Another ML platform that you can now use with Vertica. A win-win situation! Saagie is a Dataops platform that offers various ready-to-use technologies and advanced pipelines that enable you to manage all your data projects through a single interface. Select the technology of your choice for each step of the data project – from data extraction to data visualization. VerticaPy is a python library to leverage Vertica’s machine learning capabilities. Vertica’s Analytics and Machine learni ..read more
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Unlock the Potential of Machine Learning in Vertica with Qwak: Optimizing Data and MLOPs
Vertica | Vertica Blog
by Amrita
9M ago
Leverage the best of both worlds, with Vertica and Qwak integration! Vertica’s high-performance analytics and in-database machine learning combined with Qwak’s MLOps capabilities provide a comprehensive solution for organizations seeking to operationalize their machine learning models at scale. VerticaPy is a Python library that leverages Vertica’s machine learning capabilities. Qwak, a powerful machine learning engineering platform, enables you to effortlessly manage, deploy, monitor, and optimize the entire machine learning lifecycle in production. Qwak’s automation features handle various ..read more
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