You need to start a data analytics project. What are the top data warehousing tools to consider?

When deciding between cloud-based and on-premise data warehousing tools, it is important to consider the new concept of a data lakehouse introduced by Databricks Lakehouse. While cloud-based data warehouses offer benefits such as scalability and ease of integration, Databricks Lakehouse extends these capabilities through the convergence of data lakes and data warehouses. This convergence overcomes the limitations of traditional data warehouses and enables unified analysis of raw and structured data. Unlike standalone warehouses, Databricks Lakehouse integrates seamlessly with cloud platforms, ensuring flexibility and scalability. This innovative approach allows organizations to leverage the benefits of both data lakes and warehouses.

Few points from my end: - Total Cost of Ownership (TCO): Beyond initial cost considerations, organizations should thoroughly evaluate the total cost of ownership for cloud-based data warehousing tools. While on-premise solutions may involve substantial upfront capital expenses, organizations may underestimate ongoing operational costs, such as maintenance, upgrades, and energy consumption - With respect to Scalability and costs: it's crucial to acknowledge that on-premise solutions may offer more predictable costs for organizations with consistent workloads, this helps in cost budgeting - Cross platform solutions: Evaluating the interoperability of on-premise data warehousing tools with emerging cloud services and data sources is critical.

Cloud:- scalability, performance, Security, and Integration to existing and upcoming applications On-premise:- Data Warehouses are installed and maintained on your servers and hardware

A escolha entre um ambiente de análise de dados na nuvem ou em infraestrutura local depende das necessidades do negócio, recursos disponíveis e análise de custo-benefício. é crucial entender as demandas da empresa, considerar a escalabilidade da nuvem e o controle local, além de comparar os custos a longo prazo para tomar a decis?o adequada.

Factors to consider: a. Scalability & Flexibility: Ability to accommodate growing volumes and supports different data formats, file types & storage options b. Performance: Efficient handling of complex queries & processing large datasets c. Integration with other data analytics tools, BI platforms & data sources d. Cost: Assess the total cost of ownership e. Security: Prioritize features including encryption, access controls & compliance with industry regulations f. Ease of Use: Provides a user-friendly interface for managing and querying data h. Reliability & Availability: Provides high availability, fault tolerance & disaster recovery capabilities to minimize downtime Top 3 options to consider: Amazon Redshift, Snowflake, Google BigQuery

FEw points from my end: - the often overlooked aspect is the impact of data movement on system performance. Organizations must weigh the trade-offs between ETL's structured approach and ELT's ability to distribute transformations within the data warehouse. - data engineering needs to balance between flexibility and data governance when making this decision. - The consideration of data latency and real-time processing is frequently underestimated in discussions about ETL and ELT. ETL has latency in data availability for analysis. While can impact concurrent queries and system resources during peak usage

No meu ver a escolha entre ETL e ELT depende muito do tipo de análise que será realizada nos dados e das necessidades específicas do projeto. Em muitos casos, as organiza??es podem até adotar uma combina??o de ambas as abordagens, dependendo das necessidades individuais dos diferentes conjuntos de dados.

ETL Tools extract data from various sources, transform it into a suitable format, and then load it into the data warehouse. Some popular ETL tools are Informatica PowerCenter, Talend, Apache NiFi ELT tools load raw data into the data warehouse first, then perform transformations as needed within the database. ELT tools are Matillion, Stitch Data, Apache Spark

I would suggest to decide on 3 main points which method to use. 1-Data Volume: ELT is often more suitable for large data volumes as it leverages the power of modern data warehouses to handle transformations. ETL might be more suitable for smaller data volumes or when the transformations are complex. 2-Data Complexity: If your data is complex and requires significant transformations, ETL might be a better choice as it allows for data cleaning and validation before loading. 3-Real-time Needs: If you need real-time data processing, ELT might be a better fit as it allows for quicker loading of data into the system.

ELT should be used when multiple teams are involved in transforming data or working in silos using their business logic and then subsequently landing it to the gold layer (if following medallion architecture). ETL on the other hand is widely used and adopted when data volume is huge.

Additional points from my end: -Organizations may neglect to consider how easily a data warehouse tool accommodates changes to the underlying data model over time. A robust solution should provide mechanisms for seamless schema evolution. - organizations might sometime underestimate efficiency of these tools when dealing with intricate queries. Evaluating the performance of data warehousing tools is crucial to ensure optimal query execution and maintain satisfactory response times for advanced analytics - Comprehensive metadata management is essential for understanding data origins, transformations, and dependencies, providing transparency into the data lifecycle.

The efficiency of a Data Warehouse lies in its ability to give apt data quickly. Always model the Data Warehouse keeping in mind the kpi reporting needs. It is better to Not keep the grain of tables at the lowest level, rather at the levels which will suffice reporting needs. Aggregation and de normalisation will ensure fast Query execution by reducing the need of multiple joins. Historical data should be limited to timeframe at frequent reports are pulled. Archival of old data will further help in query performance.

Data modeling and querying tools are crucial for designing and optimizing the structure of the data warehouse and querying the data for analysis. Popular data modeling tools include ERwin, ER/Studio, and Oracle SQL Developer Data Modeler. These tools help in creating and managing data models, defining relationships, and ensuring data integrity. For querying data, SQL-based tools like Microsoft SQL Server, Oracle Database, and PostgreSQL are widely used. Additionally, tools like Tableau, Power BI, and Looker provide intuitive interfaces for querying and visualizing data, enabling business users to easily explore and analyze data.

In terms of data warehousing, I enjoy microsoft SQL server the most. SQL is relatively easy to learn and is so powerful, you cannot go wrong with it.

The best way to do Data modelling is by identifying Fact and Dimensional data. You can do this by following three levels. Conceptual level, Logical level and Physical level. In Conceptual level you have to make sure that the data which we collected and going to be stored is meaning full and will be used in future. In Logical level you have to decide data constraints, pk, fk it is like defining rules and regulation. And in Physical level you have to define table, views, SP. The fact data would be aggregable, measurable and transactive data like sales , profit. Dimensional data should be supportive to fact data, descriptive, filterable, etc.

An unusual but important aspect is the compatibility with new paradigms such as the Lakehouse architecture. Unlike traditional data warehousing, Lakehouse combines the strengths of data lakes and data warehouses and enables unified data management and analysis. Evaluate tools not only on their standalone capabilities, but also on their adaptability to evolving architectures. Ensuring compatibility with modern, integrated approaches like Lakehouse can improve the flexibility, scalability and seamless convergence of data processing and analytics capabilities. This forward-looking perspective aligns tool selection with the evolution of data technology.

- Choose tools that can scale with your growing data needs. - Ensure seamless integration with existing systems and data sources. - Prioritize tools with robust security features to protect sensitive data. - Consider the total cost of ownership, including licensing, infrastructure, and maintenance. - Opt for tools with a strong community for knowledge sharing and issue resolution.

Data analytical projects can be varying; and can be connected with other analytical projects to work: ?? Top-down data warehousing approach: build a enterprise data warehouse component; first build and use a operational data store component to feed it to update and insert data. ?? Bottom-up data warehousing approach: first build a data marts, a build a data warehouse component based in these previous components. ??Depend how create required components around of data warehouse component; or don't create those; you will improve your data warehouse component.

For data warehousing in a data analytics project some popular tools I'd consider are: ? Amazon Redshift ? Google BigQuery ? Microsoft Azure Synapse Analytics ? Apache Hive ? Oracle Data Warehouse ? Snowflake ? Teradata These tools help manage, process, and analyze large datasets, enabling faster insights and better decision-making. Each has its strengths and considerations, and the choice often depends on the project's specific needs, scalability, and integration requirements.

If none of the existing tools align with your functional needs, why not explore the option of developing your own orchestration tool from the ground up ? This is a viable approach worth considering. ??