Francisco Sanchez, Chief Information Officer (CIO) of Fermax, shares his insights around the early stages of IoT implementation. Watch as he delves into the importance of having connected devices and the ability to communicate with said devices in a reliable and secure way.
Learn when Fermax realized they needed to make a change and discover how they implemented that change for their business, simplifying technical complexity using Azure IoT products.
In our previous posts in this series, we spoke at length about using PgBouncer and Pgpool-II , the connection pool architecture and pros and cons of leveraging one for your PostgreSQL deployment. In our final post, we will put them head-to-head in a detailed feature comparison and compare the results of PgBouncer vs. Pgpool-II performance for your PostgreSQL hosting !
The bottom line – Pgpool-II is a great tool if you need load-balancing and high availability. Connection pooling is almost a bonus you get alongside. PgBouncer does only one thing, but does it really well. If the objective is to limit the number of connections and reduce resource consumption, PgBouncer wins hands down.
It is also perfectly fine to use both PgBouncer and Pgpool-II in a chain – you can have a PgBouncer to provide connection pooling, which talks to a Pgpool-II instance that provides high availability and load balancing. This gives you the best of both worlds!
While PgBouncer may seem to be the better option in theory, theory can often be misleading. So, we pitted the two connection poolers head-to-head, using the standard pgbench tool, to see which one provides better transactions per second throughput through a benchmark test. For good measure, we ran the same tests without a connection pooler too.
All of the PostgreSQL benchmark tests were run under the following conditions:
We ran each iteration for 5 minutes to ensure any noise averaged out. Here is how the middleware was installed:
Here are the transactions per second (TPS) results for each scenario across a range of number of clients:
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Analytics India Magazine brings the list of leading analytics and data science products for the year 2020 that have positively impacted businesses across the globe, helping them make decisions. To source the best 10 products, we reached out to more than 25 companies. Ranging from serving financial sectors to manufacturing, retail, solar and other industries, these products have made a significant impact on driving business value for the companies.
_Please note that this is not a ranking and the list is in no particular order. _
Cora Finance Analytic by Genpact is a comprehensive and persona-based financial analytics suite that enables data-driven decision making for finance professionals across all business units and functions. It aims at driving faster business outcomes by augmenting the evidence-based decision making cycle through Robust Data Foundation Layer, Advanced Analytics, Data Science Application, Domain Expertise and Smart Processes. It is built on a domain-centric data platform and can do real-time and API-based integration, including semi-structured and unstructured data.
**Key Differentiation Factor: **It is a technology platform agnostic and can be easily embedded into clients’ business workflows in any production environment — On-Prem, Cloud or Hybrid.
**Client, Sectors and Geographies Served: **It is being used by over 25 clients across sectors such as hi-tech, manufacturing and services, consumer packaged goods and retail. It serves across geographies such as the US, Europe, Asia, Australia.
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Working with the Renewable Industry (right now only Solar), Prediction by Quadrical AI helps companies maximise their returns on solar fields by assisting them in predicting with much higher precision exactly how much energy they will produce. It also points out faults, fissures and failures so they can be fixed. Using AI, they can track degradations to turn unplanned maintenance to planned maintenance. The platform further keeps learning and improving with time, reducing maintenance costs, and allowing for greater Return-on-Assets for the Solar companies.
Key Differentiation Factor: With Digital Twin technology, it aims to build an identical digital twin of the plant to make it an energy-efficient world.
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