Sql Web Server Information Tools Business Intelligence – Over the last decade, Microsoft SQL Server (or MSSQL) has competed fiercely with MySQL for second place in the most popular database ranking.
Although MSSQL and MySQL differ from each other in supported platforms, programming language and many other aspects, they have the same capabilities to work with the most popular BI tools on the market.
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Power BI is an unquestionable complement to MSSQL, as is Google Data Studio to BigQuery, because both are created by the Microsoft giant.
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Power BI is quite well known in the BI and data analytics industry, especially in enterprises that are adopting the Microsoft ecosystem. It supports an impressive number of data sources, giving businesses the ability to centralize their data in one place.
Power BI has a user-friendly interface with amazing data visualization capabilities such as interactive dashboards, numerous chart types, advanced filters, and much more.
The direct competitor of the Power BI service is , a powerful self-service business intelligence tool. In addition to a powerful visualization layer that is easy to use for non-technical users, it also includes a modeling layer that allows data scientists to model and transform data in MSSQL and many other SQL data warehouses.
The starting price of $100 per month is impressively affordable compared to all other SQL reporting tools on the market, especially when its features are just as powerful and plentiful. Even as your business grows with more analysts joining your data team or business users, you don’t have to worry about the cost of additional staffing.
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Domo is a cloud-based data analytics and BI platform for senior executives and business users who need intuitive business dashboards. Domo is often deployed in the industry with little or no support from the IT department.
Take a peek at Domo’s landing page and it’s easy to impress customers of their big brands like Cisco, ESPN and Ebay. With beautiful dashboards, a wide range of supported data sources, Domo is an easily accessible tool for many businesses.
For a more detailed list of BI tools and analytics, check out: Top BI & Data Visualization Tools for PostgreSQL Top BI & Data Visualization Tools Snowflake Best BI & Data Visualization Tools for Amazon Redshift BI Tools Benchmark Matrix: A Holistic Collection (2022)
Looker, another Google Cloud Platform product, is a powerful BI tool that provides an innovative approach to real-time data exploration and analysis.
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If you’re looking for a tool to centralize your business data, look no further as Looker with its semantic modeling layer can help you apply business logic to your data and create a single source of truth for each team.
When it comes to data visualization, Tableau is the master of my heart. While the setup process can be quite overwhelming for beginners, once you get used to it, you’ll find that other tools on the market have a long way to go to compete with Tableau in this aspect.
This is simply because Tableau has dominated the market with highly interactive dashboards, complete with a huge library of visualizations and supported metadata (check out their built-in map data!)
All of the above products have their advantages and disadvantages. Choosing the right one for your business will depend on many aspects, including company size, data maturity, budget, and importantly, the use cases you want to solve.
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This is not a one day job, so I recommend engaging all related parties (data builders and data consumers) in your company to clearly list all requirements, narrow down the list to 4-5 best tools, and try all the tools.
If you need a little more guidance on choosing the right tool, you can refer to Chapter 4 of the Analytics Installation Guide, which details how to navigate the Business Intelligence tools space.
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Check out this book to learn the ins and outs of today’s analytics toolset.
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“I’m shocked to be able to tell you this next sentence: I read a free e-book from a certain company and I really liked it.” – Data Enginee Imagine you are a business analyst at a fast fashion brand. Your job is to understand why the sales of a new clothing line in a given region are declining. Your task is to increase sales while achieving the desired profit benchmark. Some of the variables to consider are the buyer’s customer personality, website reviews, mentions on social media, sales data per store location by day and time, holiday or other event, expected payment dates at local businesses, and even heatmap data for each store and up-to-date planogram. . .
That’s a lot of different data stored in different formats. You have to extract them from different systems, or sometimes manually collect the missing data. Then, explore and visualize data by defining interconnections between events and data points to bring your data into a single warehouse. Too many dimensions, too much data to process. There must be a data management strategy and there must be an IT infrastructure to implement that strategy. This is what business intelligence (BI) is all about.
Business intelligence is the process of accessing, collecting, transforming and analyzing data in order to reveal knowledge about the company’s performance. And then use that knowledge to support decision making.
Yes, process. This means that BI includes activities, tools and infrastructure that support the transformation of data from its raw form into readable graphs. This process can be seen as a chain of successive phases:
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Phase 1. Data Extraction – Connecting to and extracting original data sources. Data sources can be internal (databases, CRM, ERP, CMS, tools such as Google Analytics or Excel) or external (order confirmations from suppliers, reviews from social networks, public repositories of data sets, etc.).
Phase 2. Data transformation – placing data in temporary storage called staging area. Formatting data according to specific requirements and standards to make it suitable for analysis.
Phase 3. Upload Data – Transfer standardized data to the final storage destination – database, data lake, or data warehouse. If necessary, creating data marts – subsets of data warehouses to store information from each unit of the company, e.g. HR or sales department.
Steps 1, 2, and 3 are combined into ETL (extract, transform, load) operations. The ETL process defines how heterogeneous data is extracted from various sources, transformed into a form suitable for analysis, and loaded into a single destination. We won’t spend much time explaining it here as we did it in a dedicated ETL developer article.
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Earlier, we wrote about the steps to implement the Business Intelligence strategy, where we touched on data integration tools and data warehouses. In this article, we will delve into the tools and services needed to establish and maintain a system-to-system data flow with further analysis and visualization.
You need the entire BI infrastructure. If you want to set up a BI process from scratch, consider vendors whose analytics solutions include modules with ETL, data warehouse, data analytics, and visualization services.
Your goal is to build a custom BI. You have tech people on board who can develop a custom BI platform or some parts of it, but are looking for building blocks. In this situation, we suggest looking at libraries, frameworks and tools that allow you to perform any stage of data processing.
First, let’s look at some A-Z solutions from leading vendors. These BI platforms include ETL and data storage services, as well as visual analytics and reports.
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We checked the Gartner 2019 Magic Quadrant for Analytics and BI Platforms (as of January 2019) and the list of the best BI programs by G2 Crowd.
Sisense is a business analytics platform that powers all BI operations, from modeling and data mining to creating dashboards. Supports on-premises, cloud, and hybrid deployment scenarios.
Data acquisition. There are two ways to get data with Sisense: importing it into ElastiCube, the solution’s proprietary database, or linking directly to the sources. The second option is called live connection and works well for frequently changing data.
Links to additional unsupported sources can be obtained from the Sisense community. See the documentation for a full list of supported data sources and types.
Online Analytical Processing (olap)
Data transformation. The platform provides many functions for data transformation. For example, it parses the spelling of attributes in tables, groups them by similarity to help users standardize attribute names. The interface makes manipulating data models simple.
Data visualization. Dashboards are made on the web. Users can add widgets to dashboards. The choice of reporting options is wide: sunburst widget, calendar heatmap, scatter map, as well as line, pie or bar charts, boxplot, polar plot and many more.
Sisense comes with tutorials, videos and documentation which are a huge help in understanding how to get the most out of the platform. To receive a price offer, please fill out the form.
Microsoft Power BI is a self-service cloud-based BI solution for exploring and analyzing visual data on-premises and in the cloud. The platform enables real-time data monitoring on almost any device across all major operating systems, from
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