How have BI queries evolved over time?

Na minha opini?o, a evolu??o das fontes de dados foi e vem sendo impressionante e desafiadora. Antigamente, a maioria dos dados vinha de sistemas internos limitados em arquivos, depois surgiram os bancos de dados relacionais, as planilhas e etc. Hoje, temos uma variedade imensa de fontes, incluindo big data, redes sociais, dispositivos IoT, e até dados em tempo real. E isso tende a crescer mais e mais! O volume de dados hoje trafegado é imenso comparado a poucos anos atrás!

The biggest current challenge in data continues to be working with large volumes and crossing different sources of information, structured and unstructured, and transforming this data into knowledge that generates value for the business. Thinking about technical operation, it is necessary to increasingly think about cloud environments, with good performance and low cost, which guarantee scalability, in addition to a high level of encryption of stored data.

Over time, Business Intelligence (BI) queries evolved: Initially, overseen by IT, data merged into single databases, queries handled by IT for less proficient users. Then, Data Discovery and Visualization enabled self-service BI, empowering analysts. Now, BI tools are advanced analytics platforms driven by AI, offering intuitive features for non-technical users, driven by the need to manage escalating data volumes and uncover vital insights for decision-making.

Business Intelligence (BI) queries have evolved significantly over time due to advancements in technology, changes in business requirements, and shifts in data processing paradigms. Here's an overview of how BI queries have evolved: Early Days - SQL-Based Queries OLAP and Multidimensional Queries Advent of Data Warehousing In-Memory Computing and Columnar Databases Big Data and NoSQL Databases Self-Service BI and Data Discovery Advanced Analytics and AI Cloud-Based BI Solutions Overall, BI queries have evolved from simple SQL-based queries to sophisticated, multidimensional analyses incorporating diverse data sources, advanced analytics, and self-service capabilities. As technology continues to advance.

BI queries have evolved from simple data retrieval with basic aggregation and filtering to complex data analysis including multidimensional analysis, predictive analytics, and integration with data visualization tools. This shift empowers users with self-service BI.

More than learning how to write the code, it is important to understand the concept behind it, what is the best decision for each context you will encounter. Of course, knowing SQL is an almost mandatory basis for working with data, but at any time you can change software and have to learn everything again. If you have the correct concepts, any adaptation and new learning becomes easier and you will be successful.

Dominant SQL: As a BI Developer, I've observed SQL's enduring strength in querying relational databases. Evolution to MDX & DAX: The emergence of MDX and DAX reflects the increasing complexity of data structures. Catering to Multidimensionality: These languages address the multidimensional nature of analytical data, allowing for advanced queries that mirror how businesses analyze information. Enhanced Data Retrieval & Storytelling: My experience shows that utilizing these languages in tools like Power BI and Tableau not only improves data retrieval but also empowers richer data storytelling. This allows me to create compelling narratives that connect with stakeholders and influence strategic business decisions.

In my experience, BI query languages have evolved to accommodate diverse data sources and analytical needs. - Initially, SQL was the primary language for querying relational databases. - With the emergence of big data, languages like HiveQL and PigLatin were developed to query Hadoop and NoSQL databases. - Specialized languages such as MDX and DAX were introduced for multidimensional analysis in OLAP environments. - Modern BI tools offer intuitive, user-friendly query interfaces, reducing the reliance on traditional query languages. - Advanced analytics platforms now support languages like Python and R for statistical analysis and machine learning within BI workflows.

BI query languages have changed over time as well. In the past, SQL was usually used to write BI queries. Today, though, there are many query languages to choose from, such as SQL, MDX, and DAX. These languages are made for different kinds of data sources and business intelligence (BI) systems and have different features.

As interfaces de consulta evoluíram de terminais de texto simples e interfaces de linha de comando para sofisticadas interfaces gráficas. Lembro como se fosse hoje, quando chegou o SQL Server 2005, foi uma mudan?a significativa de visual e intera??o em rela??o a vers?es anteriores como o SQL Server 7 ou o SQL Server 2000...

BI queries have evolved significantly, transitioning from complex SQL commands accessible only to specialists to more user-friendly query interfaces. Early systems required deep technical knowledge, limiting data access to IT departments. Modern BI tools have introduced graphical interfaces, drag-and-drop features, and natural language processing (NLP), democratizing data access and allowing users without coding skills to generate insights. This evolution towards intuitive, interactive query interfaces has vastly expanded the use of BI across organizations, enabling real-time decision-making and broader data-driven culture.

The Evolution of BI Query Interfaces Command-line Interfaces: Powerful but required specialized knowledge, limiting accessibility. Graphical User Interfaces (GUIs): Tools like Power BI and Tableau made analysis more intuitive with visual interactions, opening data exploration to a broader audience. Natural Language Interfaces (NLI): AI and Natural Language Processing (NLP) allow users to ask questions in plain language, massively reducing the technical barrier to data insights. Connection to Your Role BI Developer and Data Storyteller: This evolution supports your core mission of making data accessible and understandable to drive better business decisions.

BI's question tools have also changed over time. A command-line tool was usually used to write BI searches in the past. Nowadays, though, there are many graphical user interfaces (GUIs) that make it easier to write and run BI searches. There are many tools in these GUIs, like drag-and-drop and auto-complete, that make it easier to write complex searches.

Outro ponto bem importante... antigamente, as consultas eram bem básicas e limitadas. Hoje, os recursos de consulta incluem até processamento de linguagem natural (NLP), aprendizado de máquina para previs?es, e até análises preditivas e prescritivas. Isso significa que n?o só podemos perguntar "o que aconteceu?" mas também "o que pode acontecer?" e "o que devemos fazer?"

BI query capabilities have significantly advanced, expanding from simple data retrieval to complex analytical functions. Initially, queries were limited to basic selections and aggregations. Over time, they've grown to include predictive analytics, machine learning algorithms, and real-time data processing. This evolution has enabled users to not only query historical data but also to forecast future trends, perform what-if analyses, and generate insights from unstructured data sources. These enhanced capabilities have transformed BI from a descriptive tool into a comprehensive, predictive, and prescriptive analytics platform.

BI questions have also changed over time in terms of what they can do. When BI searches first came out, they could only get info from a single data source. BI queries today, on the other hand, can get data from many sources and do complicated sums and groupings. By adding these features, you can make BI reports that are smarter and more useful.

The integration of AI and machine learning into BI queries marks a significant evolution, shifting from manual data analysis to automated insight generation. This progression enables the identification of patterns, anomalies, and trends without explicit user queries, often in real time. AI-enhanced BI tools can now predict outcomes, offer recommendations, and even initiate actions based on data insights. This shift towards predictive analytics and proactive decision-making capabilities has greatly expanded the potential of BI systems, making them not just analytical tools but also strategic assets.