You're starting a new data warehousing project. How do you choose the right tools?

?? Before you search for tools, it's important to know what you want to achieve with your data warehousing project. Think about your business questions, performance indicators, and reporting needs. Consider how often you need to update and access your data, and how much data you have and how complex it is. By defining your goals, you can narrow down your options and focus on tools that meet your needs.

Begin by clearly defining project goals and scalability requirements, then select data warehousing tools that align with these objectives, considering factors like performance, data integration capabilities, and support for diverse data types. Embrace cloud-based solutions offering elasticity and cost-efficiency, enabling seamless scalability as your data needs evolve, and prioritize tools with advanced analytics integration for deriving actionable insights from your data warehouse. Opt for tools that prioritize security and compliance, ensuring your chosen data warehousing solution not only meets current needs but also aligns with future regulatory demands, safeguarding your data assets and ensuring long-term strategic success.

We need to define our goals before we start. First we have to identify what type of data we will be storing. What's the expected volume and growth of the data, what queries will we run, what is our budget and what level of technical expertise is available are the key points to consider.

Overall, access speed is a critical factor in ensuring that your data warehousing solution can deliver timely insights to support business decision-making processes effectively. It's essential to align the chosen tools and technologies with your specific access speed requirements to achieve optimal performance and user satisfaction. ?????

I would approach this question by emphasizing the importance of clarity and intentionality in any data warehousing project. Before diving into tool selection, it's crucial to understand the business objectives, key performance indicators (KPIs), and reporting needs. First and foremost, I would recommend sitting down with stakeholders from various departments to understand their specific requirements and the questions they need to answer using the data. This step helps in identifying the critical business questions that the data warehouse should address. For example, marketing might want to analyze customer behavior, sales might need insights into product performance, and finance might require financial forecasting data.

Employ cutting-edge data integration tools that can seamlessly connect and collect data from diverse sources, enabling a comprehensive evaluation of both structured and unstructured data for a holistic understanding of your information landscape. Leverage advanced data profiling and quality assessment tools to ensure the accuracy, completeness, and consistency of incoming data, empowering your data warehousing project with a solid foundation for reliable and insightful analytics. Choose cloud-native solutions that offer scalability and flexibility, allowing your data warehouse to adapt to evolving data sources effortlessly, and strategically position your project for future growth and innovation in a rapidly changing data landscape.

Have to evaluate the datasource based on below criteria. Performance: Can it handle your data volume and query complexity? Scalability: Can it grow with your data needs? Cost: Does it fit your budget, including infrastructure, licensing, and maintenance? Security: Does it meet your security and compliance requirements? Ease of use: Is it user-friendly for your team's technical skill level? Integrations: Does it integrate seamlessly with your existing systems and tools?

I believe it's most helpful to spend your most time in this step when migrating data to a warehouse. Understanding all of your data going in, what the pieces mean, how they plug into the business model, and the types of questions you're hoping to answer with your data are all questions that help inform how to structure warehoused data and with what connections across your business. Most any data compilation project you work on is likely going to get stuck at the "how do we connect data source A to data source B" stage so, as an added bonus of fully understanding your data sources, you can more quickly build data maps to connect the data sources and reduce your implementation timeline.

Making a robust firewall and backup to ensure continuity in case of attack or accidental loss. It is surprising to see how such issues are insufficient ku taken into consideration in small and medium sized enterprises.

Data available from a wide range of sources (private notepad, spreadsheets, ERP systems, CRM systems etc). The key is understanding the following about the data..accuracy, timeliness, reliability, consistency of measurement. If I pass my spreadsheets to someone else to complete for input into the data lake does the interpretation of the inputs remain consistent? This is about consistent, easily understood definitions. In my experience, data provided at 4.30pm may be different to the data at 6.30pm - real time activity may be a source of variation ... reports and insights extracted are as at a point in time! Need clarity about data cut off times.

A escolha entre um data warehouse on-premise ou baseado em nuvem dependerá de fatores como or?amento, escalabilidade e necessidades de seguran?a. Data warehouses baseados em nuvem, como Amazon Redshift, Google BigQuery e Snowflake, oferecem flexibilidade e escalabilidade, enquanto solu??es on-premise proporcionam controle direto sobre a infraestrutura. A natureza dos dados que você está lidando determinará se deve optar por um data warehouse relacional ou n?o relacional. Data warehouses relacionais, como SQL Server, Oracle e PostgreSQL, s?o ideais para dados estruturados, usando tabelas e esquemas definidos.

Based on your needs, we need to explore different data warehousing solutions like 1.Cloud based (Amazon Redshift, Google Big query, Azure Analytics) 2. Open-source ( PostgreSQL, Teradata, Apache Hive) 3. On premise ( Teradata database, Oracle exadata)

A data warehouse is like a central storage place where you keep and organize your data for analysis. It can be on-site or cloud-based, depending on your budget, how much you need it to grow, and how secure it needs to be. You also have to decide if you want a relational or non-relational data warehouse, based on how complicated your data is. Relational data warehouses use structured tables to store data, while non-relational ones use flexible documents. Examples of relational data warehouses include SQL Server, Oracle, and PostgreSQL. Examples of non-relational ones include Cassandra and DynamoDB.

Select a data warehouse provider that prioritizes energy efficiency and sustainable operations. Consider the provider's use of renewable energy and their overall carbon footprint. Overlooking these factors can contribute to higher operational emissions, conflicting with environmental sustainability goals.

In selecting a data warehouse, consider future scalability and the complexity of data types. Cloud-based options offer flexibility and can be more cost-effective for growing businesses. For structured, transactional data, a relational warehouse like SQL Server is ideal. For diverse, unstructured data, non-relational options like MongoDB adapt better. Prioritise integration capabilities with your current systems and ensure robust support for data security and compliance needs. This strategic approach ensures a tailored fit for your data warehousing needs - and can help you avoid getting caught out in the future!

The best ETL tool is depends on your specific needs and preferences. Take the time to evaluate your requirements, research available options, and consider various factors before making your final decision. But here i am mentioning some examples: Talend: An open-source platform with a strong community and various pre-built connectors. Consider its scalability and customization options. Informatica: A powerful, enterprise-grade solution with a robust feature set. Be mindful of its potential licensing costs. AWS Glue: A cloud-native option well-suited for AWS environments, with pay-as-you-go pricing. Evaluate its integration with your existing AWS infrastructure.

Based on my experience, this can vary from org to org. The selection of ETL tool not just based on whats great, efficient or cutting edge, it is also based on the several factors and the business for which you planning to build ETL solutions. A company who has lower margins wont invest a lot on cutting edge tools and would want to adopt open source tools. Another factor is how easily the developers are available in the market to work on the selected tool. How easily the learning content is available for that tool and how skilled the current team is to start using the tool. Just like a ML model requires correct feature selection for the model to work, a ETL tool selection requires the correct factors to be identified first.

An etl tool is software that automates getting data from your sources to your warehouse. It saves time, boosts data quality, and keeps things consistent. Choose a tool that can handle your data's volume, variety, and speed, and can connect different sources and your warehouse. Consider how easy it is to use, how well it performs, and the cost. Examples include Talend, Informatica, and AWS Glue.

For DWH and ETL we can go completely Open Source. Apache has a bouquet of tools for ETL, Data Integration and Data pipelines like NiFi, Airflow etc. For visualisations and Dashboards we can use Apache Superset. And though these tools are open source and free, they have amazingly rich and advanced features and functionalities.

As ferramentas de ETL (Extract, Transform, Load) s?o pe?as fundamentais no ecossistema de data warehousing, proporcionando automa??o e eficiência no processo de movimenta??o de dados. Diversas ferramentas de ETL est?o disponíveis no mercado, cada uma com suas características, a saber: - Talend: Uma ferramenta de ETL de código aberto com recursos robustos e uma comunidade ativa de usuários. - Informatica: Oferece uma plataforma abrangente para integra??o de dados, incluindo ETL. - AWS Glue: Uma solu??o de ETL totalmente gerenciada na nuvem, integrada ao ecossistema da Amazon Web Services.

A BI tool is software that helps you visualize, analyze, and share your data from YouTube. It creates dashboards, reports, and graphs for insights and actionable info. Choose a tool that connects to your data warehouse, meets your analytical needs, and has a user-friendly interface. Consider features, functionality, and price. Examples include Power BI and QlikView.

Choose a Business Intelligence tool that enables robust ESG reporting and analysis. The tool should facilitate insightful sustainability metrics and governance analysis. Ignoring this aspect may result in inadequate reporting capabilities, hindering your ability to track and communicate progress on ESG initiatives.

Ferramentas de BI capacitam usuários a criar dashboards, relatórios e gráficos para extrair insights valiosos dos dados armazenados em seus sistemas. Essas ferramentas facilitam a tomada de decis?es informadas, fornecendo insights claros e acessíveis. Além disso permitem o compartilhamento fácil de relatórios e dashboards, promovendo a colabora??o e auxiliam o monitoramento por meio de indicadores-chave de desempenho (KPIs).

El termino BI es tan comúnmente ocupado que en la actualidad ya todos nos etiquetamos como BI, sin embargo lo relevante es seleccionar la herramienta correcta a ocupar, en primer lugar esta debe hacerlo fácil y rápido ya que el negocio no espera, dos, como tropicalizas a los diferentes tipos de mercado la herramienta de inteligencia, no es lo mismo analizar Retail que Educación o Telecomunicaciones y tres, como eso ayuda a la toma de decisiones que impacten en mas rentabilidad

Elegiría Tableau como herramienta de BI debido a su capacidad para manejar grandes volúmenes de datos y su interfaz intuitiva, que facilita la visualización de datos complejos de manera sencilla y dinámica. Tableau es reconocido por su flexibilidad en la conexión con diversas fuentes de datos y por permitir a los usuarios crear paneles y reportes personalizados sin necesidad de tener conocimientos avanzados en análisis de datos. Esta combinación de facilidad de uso, potencia y flexibilidad lo convierte en una opción atractiva para una amplia gama de usuarios y organizaciones.

Implement a continuous evaluation process for your tools' performance and sustainability impact. This ensures that your data warehousing ecosystem remains efficient and aligned with ESG objectives. Failure to regularly assess and refine your tools can lead to sustained inefficiencies and a drift away from sustainability commitments.

Teste a integra??o e a intera??o entre suas ferramentas de ETL, data warehouse e BI. Garanta que elas funcionem de maneira sinérgica, proporcionando uma experiência contínua e eficiente. Avalie a robustez das medidas de seguran?a implementadas em cada fase do processo, desde a extra??o até a visualiza??o. A seguran?a dos dados é essencial para proteger informa??es sensíveis.

Apesar de todas as etapas serem extremamente técnicas e absolutamente corretas, considero importante elucidar que todas exigem colaboradores capacitados para atuar com maestria os referidos sistemas, pois apesar de super eficientes, as plataformas e até mesmo todo o planejamento de instala??o, opera??o e manuten??o precisam dessa Expertise. Assim sendo, eu incluiria que paralelo a todo processo, a escolha da equipe é t?o importante quanto a defini??o dos softwares e sistemas a serem utilizados.

All employees were trained to upload data relevant to their roles into the designated space. Additionally, training was provided on utilizing the warehouse for data retrieval. The warehouse is structured with tiered permissions, granting full download and retrieval access, view-only access, or restricted access to specific areas based on administrative levels. Throughout the initial training phase, we collected notes and feedback, which were shared with the technology team for problem-solving and improvement. Testing is an ongoing process, requiring periodic note-taking and reviews to ensure continuous enhancement.

This last part of the project before operation request time and people to work it out. At this moment you will have to make choices betwenn useful or not indicators, to develop new indicators can also be an option.

Before undertaking a data warehouse project- the company - -should take an inventory and understand all of the data or data access that the company has in house. The company has either purchased the data or has access to the data. . -questions should be asked about the utility of the data- do we need the data? Are there other data sets that the company should be purchasing that could be put to better use? -a wish list of output or necessary data should be put together including a Dashboard Report that would have the most utility for the company. - This exercise will lead to the company purchasing data and tools - they can afford - tools that make sense for what output the company needs and include as much future growth as possible.

It is important to assess the scalability of the platform you are adopting. At the same time, dont go for an overkill by investing in something that is much more than what you need. Important is to see that the platform is Fit for Purpose and at the same time Fit for Future!

We went through a similar experience. We decided to store the organization's data in a single digital repository to make it easy to retrieve and use, to reduce time, reduce the use of paper, and facilitate analysis and other purposes. We defined the goal of the project, but we did not take enough time to develop appropriate key indicators for the success of the project, as the team working on the project was very diverse team with weak collaboration. I believe that spending time in defining requirements, goals, and setting indicators - even if it takes a long time at the beginning - lays a strong foundation and saves a lot of time for subsequent work.

As with any technology choice, it's ultimately about trade-offs, so it's important to be clear on the requirements not only today, but how they might evolve in the future - think Total Cost of Ownership, not just up-front build costs. How much is your data model likely to change? (Easier with some technologies than others) How much might volumes grow? Be sure that your choice can grow/flex easily to accommodate likely future scenarios.

Most important step that needs to be added here- you must identify your power users and how you will use them through out your process. Especially before you choose a system. I’ve been through too many integrations that the key users were bought in after the ink was on the contract and found out there were so many business concerns not raised and addressed at the start….