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

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Data analytics is the process of transforming, modeling, and visualizing data to generate insights and support decision making. To perform data analytics effectively, you need to have access to reliable, relevant, and timely data. This is where data warehousing and business intelligence tools come in. Data warehousing is the practice of storing and organizing data from various sources in a centralized location, while business intelligence is the application of analytical techniques and tools to extract, process, and present data in a meaningful way. In this article, we will explore some of the most effective data warehousing and business intelligence tools for data analytics, and how they can help you achieve your goals.

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Rob Wozniak

Data Architect
Phyoe Khaing Min

IT Research & Development | IT Software Engineering | Visionary Leadership | Agile Application Development Management |…
Christian Pohlmann

Data & AI in Google Cloud

1 Data Warehousing Tools

Data warehousing tools are software solutions that help you design, build, manage, and optimize your data warehouse. A data warehouse is a repository of integrated data that can be used for analytical purposes. Data warehousing tools can help with tasks such as data extraction, transformation, loading, cleansing, validation, integration, security, and backup. Some of the most powerful data warehousing tools for analytics are Amazon Redshift, a cloud-based data warehouse service that offers fast and cost-effective storage and query capabilities; Microsoft Azure SQL Data Warehouse, a cloud-based data warehouse service that integrates with Microsoft Azure services and supports various data formats; Snowflake, a cloud-based platform that enables data sharing and collaboration; Oracle Database, a relational database management system with comprehensive data warehousing features; and Teradata, a platform providing high performance storage and processing capabilities for large datasets.

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Rob Wozniak

Data Architect
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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.

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Phyoe Khaing Min

IT Research & Development | IT Software Engineering | Visionary Leadership | Agile Application Development Management | Research & Development (Lean Approach) | Project Management | Data Analytics | Database Architecture
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In my experience, I prefer to use AWS Cloud Services. For ETL Tools, will be used Glue Jobs to get raw and try to do Data cleansing, modelling at Redshift. For Analytic and Dashboard will connect with MS Power BI and Excel for detail analytic.

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Christian Pohlmann

Data & AI in Google Cloud
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For me Snowflake and BigQuery are the best environments for building a modern scalable DWH. Use dbt and Fivetran or Airbyte for source integration and Looker or Tableau for the visualizations & self-service and nothing can stop you (technically). Of course understanding the needs and business cases + a good data strategy is even more important than the technical setup.

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Sumit C

Sr BI/BO Consultant
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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)

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Oghenebrukeme Begusa

Business Intelligent Analyst @ Unified Payment Services Limited | Data Analysis | Project Support Officer | Business Analyst | Fintech | Digital Payment | Financial Inclusion | Virtual Assistant
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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.

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2 Business Intelligence Tools

Business intelligence tools are software solutions that enable you to analyze, visualize, and communicate data in a user-friendly and interactive way. These tools can help you with tasks such as data exploration, discovery, reporting, dashboarding, storytelling, and collaboration. Some of the most effective business intelligence tools for data analytics include Tableau, Power BI, Qlik Sense, Looker, and SAS Visual Analytics. Tableau provides stunning and insightful charts, graphs, maps, and dashboards from various data sources and formats. Power BI integrates with Microsoft products and services to create and share interactive reports, dashboards, and apps. Qlik Sense leverages artificial intelligence and natural language processing to help you discover and explore data. Looker connects to various data sources and platforms to create customizable reports, dashboards, and applications. SAS Visual Analytics combines advanced analytics and machine learning with interactive graphics and dashboards that support various data sources and formats.

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Roopa Mitta

Data & Analytics Leadership | Presales & Consulting | Strategy & GTM
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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.

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Aditya Jain

Architect - Data Analytics & BI | Gen AI | Domo Partner | Power BI | Tableau | Decision Intelligence
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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.

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3 How to Choose the Right Tools

Choosing the right data warehousing and business intelligence tools for data analytics is dependent on multiple factors, such as the sources, formats, volumes, and complexity of your data, your analytical objectives and requirements, your budget and resources, your technical skills and preferences, and your organizational culture and needs. To ensure you select the best tools for your data analytics projects, you should define your goals and scope; assess the availability and quality of your data; identify your data warehousing and business intelligence needs; compare different options; and test and select the most suitable ones. Defining your goals will help you determine what questions you need to answer or problems you need to solve. Assessing your data will give you an understanding of its reliability, relevance, timeliness, formats, structures, and volumes. Identifying your needs will help you understand which features and functions are necessary from the data warehousing and business intelligence tools. Comparing different options will enable you to evaluate their pros and cons in relation to your data availability and quality as well as your needs. And testing the performance, functionality, usability, security, compliance, as well as collecting feedback from stakeholders will help you make an informed decision.

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Julius S.

Sub Saharan African Senior Researcher specializing in Data Analysis and and Field work Dynamics
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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

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Aditya Jain

Architect - Data Analytics & BI | Gen AI | Domo Partner | Power BI | Tableau | Decision Intelligence
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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

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4 How to Use the Tools Effectively

Once you have chosen the best data warehousing and business intelligence tools for your data analytics projects, you need to use them effectively to achieve your desired results. To do this, it is important to plan and design your data warehouse and business intelligence solutions, considering how to structure and organize them, as well as ensuring data quality, consistency, and integrity. Additionally, you must implement and deploy your solutions by extracting, transforming, loading, cleaning, validating, and integrating data from various sources into your data warehouse. You must also create, configure, and customize your business intelligence reports, dashboards, and applications. Furthermore, you must monitor and maintain your data warehouse and business intelligence solutions by monitoring their performance, functionality, and usability. Additionally, you must identify and resolve any issues that arise. Finally, it is important to analyze and communicate your data warehouse and business intelligence solutions by exploring and discovering data to generate and test hypotheses. You should also present and share your findings to your data analytics team and stakeholders.

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5 Benefits of Data Warehousing and Business Intelligence Tools

Data warehousing and business intelligence tools can provide numerous advantages for data analytics. These tools can improve data quality, reliability, and accessibility by collecting, storing, organizing, and managing data from various sources in a centralized and standardized way. Additionally, they can enhance data analysis, visualization, and communication by applying analytical techniques to data and displaying it in a user-friendly and interactive way. Furthermore, these tools can support data-driven decision making and action by generating insights and recommendations from data. Finally, they can increase data analytics efficiency and effectiveness by streamlining and automating processes and tasks.

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Aditya Jain

Architect - Data Analytics & BI | Gen AI | Domo Partner | Power BI | Tableau | Decision Intelligence
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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

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Aditya Jain

Architect - Data Analytics & BI | Gen AI | Domo Partner | Power BI | Tableau | Decision Intelligence
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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

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Roderick Fernan

Senior Data Analyst / Data Engineer at Millicom International Services LLC
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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".

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

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1 Data Warehousing Tools

2 Business Intelligence Tools

3 How to Choose the Right Tools

4 How to Use the Tools Effectively

5 Benefits of Data Warehousing and Business Intelligence Tools

6 Here’s what else to consider

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