You’re trying to make sense of your data. Which data warehousing tools should you use?

You shouldn't be starting by selecting a DW tool, you should be starting by understanding what business problems you are trying to solve, which business capabilities will be required to solve these problems, which technology capabilities will be required to support them, then what logical architecture can enable those technology capabilities, and that logical architecture, in combination with cost and talent pool considerations will drive the decisions on tool selection.

Neste ponto comento... se já tem uma infraestrutura on-premise robusta e equipe para manter, fa?a localmente! Sen?o, coloca em cloud (lhe dará maior flexibilidade e escalabilidade, reduzindo dores de cabe?as)

Yes! First and foremost, what BUSINESS problem are you trying to solve?? Understand this first. Align desired capabilities back to the Business GAP and desired OUTCOME.

1.Snowflake ( if you can maintain the high cost) 2.Google Big Query ( second preference) 3.AWS Redshift/Azure Synapse ( if there is a need for the specific cloud native solution and is using the same cloud for other data engineering issues. Exactly in the order above.

Data warehousing tools are software applications that help you create, manage, and query your data warehouse. Some of the common data warehousing tools are: - Microsoft Azure Synapse Analytics: A unified analytics platform that combines data warehousing, big data processing, and machine learning capabilities. - Amazon Redshift: A cloud-based data warehouse service that offers fast performance, scalability, and security. - Google BigQuery: A serverless data warehouse that enables you to run complex queries on petabytes of data in seconds. - Snowflake: A cloud data platform that supports multiple data types, formats, and sources, and provides a high level of concurrency and performance.

A key challenge continuosly addressed by Data Architectures is the ease of reporting. Business users always seek to integrate, and access new data faster, fueling the need for self-service. This highlights Data Governance as the top priority to facilitate comprehensive data exploration and discovery within the Data Warehouses and Data Lakes. Emerging paradigms like Data Valult, Data Mesh, and Data Lakehouse, are aimed at providing enhanced data governance and data documentation practices. While dedicated tools exist for maintaining documentation about data, technologies like SAP Data Sphere, Databricks Unity Catalog, Microsoft Fabric are pioneering implicit approaches to tackle this. We move towards an era of decentralized data storage!

Data warehousing architecture is the structural design that facilitates the efficient storage, retrieval, and analysis of data within a data warehouse. It typically includes components like ETL (Extract, Transform, Load) processes for data integration, a data warehouse itself for storage, and tools for querying and reporting. The architecture aims to optimize data accessibility, performance, and analytical capabilities, ensuring organizations can derive meaningful insights from their data.

Shifting to a modern, cloud-based architecture not only resolved these issues but also improved scalability. This shift facilitated easier integration of new data sources and empowered our analytics teams with faster, more reliable access to critical insights. Implementing a data lake within the architecture enhances flexibility and accommodates diverse data types. Leveraging modern ETL (Extract, Transform, Load) tools simplifies data movement and transformation processes. Regularly assessing the architecture's performance ensures it aligns with evolving business needs, promoting efficiency in data processing and analysis. A well-architected data warehouse lays the groundwork for streamlined operations and data-driven decision-making.

When designing a data warehouse, choosing the right architecture is crucial, with two common types: dimensional and relational. Dimensional warehouses use a star or snowflake schema, centralizing data in a fact table surrounded by dimension tables describing data attributes. This structure excels in supporting analytical queries. On the other hand, relational warehouses employ a normalized schema with multiple interlinked tables via primary and foreign keys, ideal for transactional queries. Each type caters to different data management needs, with dimensional being more analytics-focused and relational better suited for detailed transactional data handling.

Employ data modeling tools to design and visualize the structure of your data warehouse. Tools like ERwin, IBM Data Architect, or Microsoft Visio can assist in creating and managing data models.

Data warehousing modeling involves structuring data in a way that supports effective querying and reporting within a data warehouse. Two common approaches for data integration in this context are ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform). ETL and ELT depends on factors such as data complexity, processing capabilities, and specific business requirements. ETL is often used for structured data transformations, while ELT is suitable for scenarios where the processing power of the data warehouse can handle transformation tasks effectively.

Effective data warehouse modeling is about creating structures that align with business needs. Employing dimensional modeling techniques like star and snowflake schemas simplifies complex data relationships. This approach enhances query performance, making it easier to extract meaningful insights. By adopting a star schema for our data warehouse model, we resolved integration issues, streamlined reporting, and ensured data consistency. Collaborating closely with business stakeholders helps in identifying key dimensions and facts, ensuring the model accurately reflects the organization's data landscape. A well-designed data warehouse model forms the foundation for successful analytics and reporting.

When modeling and transforming data in a data warehouse, you can choose between two approaches: ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform). ETL involves extracting data from the source, transforming it (like cleaning and organizing), and then loading it into the warehouse. This method is efficient for structured and clean data. Conversely, ELT involves extracting data, loading it directly into the warehouse, and then performing transformations within the warehouse itself. This approach is more flexible for handling unstructured and complex data. Tools like Talend, Informatica, and Stitch are commonly used for these processes, each aligning with the specific requirements and complexities of the data being handled.

If your data requires complex transformations, data cleaning, or enrichment, ETL may be the preferred approach. ETL tools often offer a graphical interface for designing intricate transformation logic.

I would also say a data warehouse which collaborates well with visualisation tool should be given highest preference. Ease of use and maintenance should also be considered. Low code tools can also be considered based on the available teams' expertise.

Ferramentas de BI (ou de visualiza??o de dados) devem ser consideradas antes mesmo de definir o modelo de data warehouse a ser constuído, visto que algumas ferramentas s?o projetadas para ter melhor performance com o modelo star schema, como Oracle e Power BI, enquanto outras para ter melhor performance com modelo snow flake, como o Microstrategy.

When deciding how to access and query data, you can choose between SQL and BI tools. SQL, or Structured Query Language, is a standard for data manipulation and retrieval in databases, offering precision and power. Tools like DBeaver, SQL Workbench, and DataGrip exemplify this category. On the other hand, Business Intelligence (BI) tools like Tableau, Power BI, and Looker focus on analyzing and visualizing data, offering a more user-friendly and interactive experience for gaining insights and aiding decision-making. While SQL tools excel in detailed data handling, BI tools are superior for visual data interpretation.

There are several data warehousing tools available, each with its own strengths and features. Some popular options include Amazon Redshift, Google BigQuery, Snowflake, Microsoft Azure Synapse Analytics, and Apache Hive. The choice of tool depends on factors such as your specific data requirements, budget, integration capabilities, and scalability needs.

Data warehousing access involves defining how users interact with and retrieve information from a data warehouse. Choosing between SQL (Structured Query Language) and BI (Business Intelligence) tools is a critical aspect of this process. The choice between SQL and BI tools depends on the user base and their technical proficiency. SQL is preferable for users comfortable with writing queries, while BI tools offer a more intuitive experience for those who prefer a graphical interface. In many scenarios, a combination of both SQL and BI tools is used to cater to diverse user needs within an organization.

Data warehousing security involves implementing measures to protect the confidentiality, integrity, and availability of data stored in a data warehouse. This includes access controls, encryption, authentication, and auditing mechanisms. Security measures aim to prevent unauthorized access, ensure data accuracy, and safeguard against potential threats or breaches. Regular monitoring, compliance with industry regulations, and a comprehensive security strategy are essential components of a robust data warehousing security framework.

Implement static data masking for non-production environments and dynamic masking for production environments. Dynamic masking adjusts data visibility based on user roles and permissions.

Você está tentando entender seus dados. Quais ferramentas de data warehouse você deve usar? O artigo tem o título acima e pra mim a resposta a esta pergunta seria: 1. Entenda a necessidade do negócio, entenda o problema! 2. Entenda o público 3. Entenda os dados (pelo artigo, creio que essa seria a fase mais importante), uma boa análise exploratória de dados, lhe fará entender como o dado tá disposto. 4. Com base no entendimento da fase anterior, você deve definir a melhor estratégia para modelar seus dados (quais fatos, quais dimens?es e etc) 5. A ferramenta de ETL e ou ferramenta que irá suportar esse DW, no meu ver, é o de menos! O dados estando entendido e modelado corretamente, a ferramenta é a que melhor se encaixe em custo/benefício.