What are the most effective data warehousing and business intelligence tools for data analytics?

One thing that really distinguishes cloud data platforms like Snowflake from traditional, on-prem database solutions is the separation of storage and compute. This is significant because the processing power you need for query processing, data integration and transformation can be scaled separately from what's required for data storage.

SAP is having best products in the market, have been using from last more then 15 years for ex (SAP Business Objects, S4 HANA, BODS etc)

Tableau and Power BI are one of the best business intelligence tools to enable stronger data analysis, more accurate reporting, and enhanced operations through optimization.

En mi opinión, como consultor de tecnologías SAP. Puedo hablar de lo que mejor funciona cuando el erp es de SAP Las herramientas de inteligencia empresarial (BI) y almacenamiento de datos más adecuadas son: 1. SAP BW (Business Warehouse): diseñada específicamente para integrarse con los sistemas SAP. Proporciona capacidades de extracción, transformación y carga (ETL), así como funciones de modelado de datos y gestión de metadatos. Esto lo incorpora por estándar al igual que el soporte para datos maestros y jerarquías. 2. SAP HANA: Esta base de datos potenciara a BW con procesamiento en memoria. 4. Microsoft Power BI: Es una solución de análisis en la nube que combina funciones de BI, planificación empresarial y análisis predictivo.

There are multiple dataware housing tools are available in the market. Some of the commonly used data warehousing tools are 1. Amazon Redshift - A fully managed data warehouse service by Amazon Web Services (AWS) with powerful query performance 2. Google big Query - Google Cloud's serverless data warehouse that enables super-fast SQL queries 3. Snowflake - A cloud-based data warehousing platform known for its flexibility, scalability 4. Teradata - A long-established data warehousing solution known for its performance, scalability, and support for complex analytical queries 5. Lumenore - A cloud based End to end BI tool having Data warehouse capabilities Based on your need and requirement you need to select the correct tool.

In my experience, thinking BI beyond Enterprise Grade Analytical Dashboarding & Reporting is a game changer. Few key areas include: * Problem solving through pattern recognition, a well established use case for a BI Tool and would remain an important area Ex: Excel(Yes, will still remain a powerful tool for quick analysis), Power BI, Tableau, Looker etc. * Search based BI tools that are best suited for ad-hoc and self service insight generation. Ex: Thoughtspot * Augmenting it with latest innovations in Gen AI would be another key differentiator. Some key use cases we are exploring in this area include ** Developer / Analyst Experience ** End Consumer Experience & Collaboration ** Automated Discovery & Analysis and so on.

There are many tools in the market as of now and most of them are trying to imitate each other which is good in a way that they all try to give what the industry demands. Domo, Power BI and Tableau are the tools that I have used and I really like them.

TRUST ME YOU WILL NEVER START IF YOU DON'T START. At the Advent of Python, as a programming language, I struggled to choose between Python, c++ and other languages But in data analysis and warehouse, once I learned big Query, I stick with it Today once you choose a warehouse that can accommodate your needs, you stick to that since, new applications will keep coming up

BI is basically Data Ingestion, Processing and Visualization. Hence when looking for a BI tool, check these points- 1. If it can handle the volume of data you want it to 2. Can it provide enough data processing options 3. How are its visualization and collaboration feature. 4. Is the tool on Cloud or not (Cloud is preferable) 5. Do they have enough developers in the market 6. How is the tool's support and documentation? So this would be a mix of your homework + market research about the tools. Check your requirements, define your target and see if the tool is able to meet it and also check if you have the correct resources to put to work on the tools. Thanks

Data Warehousing as the name suggests builds a warehouse of data. It would be immensely helpful if you have data coming from various sources which are On Prem, on Cloud, flat files, emails etc. You can consolidate all of them via data pipelines to create a warehouse. It will keep all your data handy. BI Tools are very much needed in today's world where people are no more into guess works but rely on data to make decisions. BI tools enable the users to collaborate and get actionable insights from your data and plan the business strategies accordingly. In today's era, investing in a good BI tool would be one of the best decisions. Thanks

I would also like to add that for Warehousing and BI Development you would find great tools in the market but do no follow only the tools, you would require good understanding of the concepts yourself and need a team of good resources who would not see data in silos in association with the business you are into and help you create stories that the data is trying to tell you. Concepts and Technologies are always greater than Tools Thanks

defining an effective dwh & tools for analytics requires deep understanding of used cases. there is no single soln that supports all used cases. that said, let's catagorize used cases based on level of security, compliance, privacy, realiability, speed, concurrency, acid, stream, time series, index search, iot, image, video, document, private, public, hybrid cloud & a dozen more. as you handle more used cases, you will realize that there is no single solution that covers all. practical used cases are often mixed & evolving. i would recommend, however, that best strategy to find the best solution is to use a decision tree that correlates used case, category, solution, tools & COST. As aws says, "use the right tool for the right purpose".