Você está iniciando um novo projeto de data warehouse. Quais são as melhores ferramentas de gerenciamento de banco de dados para usar?

When starting a new data warehousing project, the choice of database management tools can significantly impact the success of your project. Tools like Amazon Redshift, MS Azure SQL Data Warehouse, and Snowflake are some of the best in the market. Amazon Redshift is a fast, scalable data warehouse that makes it simple and cost-effective to analyze all your data across your data warehouse and data lake. highly scalable, and cost-effective multi-cloud data warehouse designed for business agility. Microsoft Azure SQL Data Warehouse is an analytics service that brings together big data and data warehousing for easy analysis of data. Snowflake is a cloud-based data warehousing platform that allows users to manage and analyze large data volumes.

I want to call out the difference between database management tools and database management systems. Tools for modeling, information architecture, and metadata management are much different than the common DBMS platforms. Secondly, while the relational databases are still powerful and well aligned for data warehouse (OLAP) workloads, there are better hybrid, or multi-model, solutions that can support structured data in relational tables and in documents. Personally I would recommend PostGreSQL as a multi-model database management platform as Snowflake and AWS Redshift (and others) offer protocol level compatibility.

SQL-based tools are ubiquitous for managing databases in data warehousing. SQL (Structured Query Language) serves as the standard for accessing and manipulating data in relational databases. These databases organize data into tables, columns, and rows, establishing relationships with primary and foreign keys. SQL-based tools enable the creation, modification, and querying of data using SQL commands and functions. Widely used tools for data warehousing include MySQL, PostgreSQL, Oracle, Microsoft SQL Server, and IBM Db2.

SQL provides a powerful set of features for aggregating, filtering, and analyzing large datasets, making it well-suited for business intelligence and reporting purposes. Additionally, the standardized nature of SQL allows for seamless integration with various reporting and visualization tools, enhancing the overall analytical capabilities of the data warehouse. This compatibility ensures that data analysts and decision-makers can easily extract valuable insights from the stored information, contributing to informed decision-making within the organization.

Define Requirements: Understand project needs and data volume. Consider Scalability: Choose tools that scale with data growth. Evaluate Integration: Ensure compatibility with existing systems. Prioritize Security: Select tools with robust security features. Review Support and Community: Look for active support and user communities

La mise en place d'entrepôt de données doit passer par le choix d'outil d'ETL et l'analyse/inventaire des sources afin de préparer au mieux son chargement. Avec ces éléments, la modélisation peut suivre différentes modélisations classiques : Etoile, Data vault, ... Bien que de plus en plus d'outils permettent d'effacer certains aspects de la création via des interfaces graphiques, il ne s'agit que d'une surcouche propriétaire vous empêchant de conserver la maîtrise de vos données. Une base de données SQL peut être branché sur n'importe quel outil et analyser par la suite. D'autant plus que la langage SQL est aujourd'hui de plus en plus répandu.

Consider NoSQL-based tools as an alternative for database management in data warehousing. NoSQL, or Not Only SQL, indicates that these tools diverge from SQL as their primary query language and do not strictly adhere to the relational model. NoSQL tools leverage diverse data structures like key-value pairs, documents, graphs, or columns for data storage and access. They excel in handling unstructured, semi-structured, or heterogeneous data, as well as scaling horizontally across multiple nodes. Prominent NoSQL-based tools for data warehousing include MongoDB, Cassandra, Neo4j, Redis, and HBase.

Explore cloud-based tools as an alternative for data warehousing database management. Hosted and managed by third-party providers like Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure, these tools bring scalability, elasticity, availability, security, and cost-effectiveness to data warehousing. Additionally, cloud-based tools offer diverse features and services such as data integration, data quality, data governance, data analytics, and data visualization. Prominent options in this category include Amazon Redshift, Google BigQuery, Microsoft Azure SQL Data Warehouse, and Snowflake.

Cloud based platforms are the also the best for scaling your database, instead of having to purchase a new server, all you need is to upgrade your plan when using tools like azure , which allows for easy scalability, and most of the management is done on their own side, so you don't have to be bothered about , power, security and the likes Cloud tools is the best decision I personally think you can make for your company.

Selecting the optimal database management tool for your data warehousing project hinges on numerous factors: data sources, types, volume, velocity, quality, security, analysis, and visualization needs. Consider budget, resources, skills, and preferences. No one-size-fits-all solution exists, prompting a thorough evaluation of each tool's pros and cons based on your specific requirements and goals. Embrace a hybrid or polyglot approach, leveraging multiple tools to optimize data warehousing performance and functionality. This nuanced selection ensures alignment with your project's unique demands and maximizes the effectiveness of your data management and analysis endeavors.