Data tools are essential for data architects who need to design, build, and manage data systems. However, using a single tool for every task may not be the best option. In this article, you will learn how to combine different data tools for better results, such as faster performance, higher quality, and more flexibility.
Data tools can be categorized into four main types: data modeling, data integration, data analysis, and data visualization. Each type has its own strengths and weaknesses, and may not be suitable for every scenario. For example, data modeling tools can help you create logical and physical schemas, but they may not support all the data sources and formats you need. Data integration tools can help you move and transform data, but they may not handle complex business logic or data quality issues. Data analysis tools can help you explore and query data, but they may not provide the best visualizations or dashboards. Data visualization tools can help you present and communicate data, but they may not offer the most advanced analytics or customization options.
By combining different data tools, you can leverage their complementary features and capabilities, and overcome their limitations. For instance, you can use a data modeling tool to design your data structures, a data integration tool to populate and update your data warehouse, a data analysis tool to perform complex queries and calculations, and a data visualization tool to create interactive reports and charts. This way, you can achieve better results than using a single tool for everything.
The only tools I've combined are Fivetran + dBt + Snowflake and Airflow + dBt + Redshift. Integrate is a better word than combine. These two combinations of integrated tools made data ingestion, data transformation and storage as easy to use that I had everyone on the team building data pipelines "self serve". I've also used Monte Carlo for Data Observability, Collibra as a Data Catalog and have built my own Data Catalog (see my post in my LinkedIn profile). I've used old school data modeling tools but have heard great things about SQLdbm. I am enjoying using Coginiti for SQL and MS Visual Studio Code for Python. There are quite a few excellent plugins for Visual Code like Github and Snowflake and Python.
In my experience you can combine data tools for better results by doing the following : - Defining objectives - Assessing current tools - Integrating data sources - Cleaning and storing data - Analyzing with appropriate tools - Visualizing insights - Automating processes - Utilizing ML/AI - Ensuring security and governance - Monitoring performance - Promoting collaboration - Continuous learning and feedback. This optimizes data utilization and drives improved outcomes.
If you have a single tool that does it all for your particular case, then I would say it is a miracle. In my experience I have always used at least two tools, even for the simplest of the tasks. Let's say you need to make data visualization there are at least two tools - one for data storage and one for visualization. And if the data must be update frequently you need yet another tool to do exactly that. So, in my experience, combining data tools is just a natural order of things, and the amount of tools combined is only dependent on the complexity of the task at hand.
rarely one data tool will work for data analysis, usually one tool for cleaning, extraction and one for visualization will do. If your data is present in lots of tables, then a sql query will help in getting them in one table, use that to connect to a viz tool like tableau, power bi
Combining Data Tools can help you create pipeline of your own tech stack, whether you choose to use cloud technologies or not. To start with combining data tools for better results: - Choose between licenced or open source (do your POC for long term goals) ex: AWS data tools support only few doc/file types. - How compatible are they for additional features - Always test dry run and major updates of tools before committing to one tool, keep options in mind.(Documentation saves a lot of time in development do read them)
When selecting data tools, you need to consider many factors, such as the type and volume of data you have and need, the complexity and frequency of data processes and workflows, the level of automation and scalability you require, the budget and resources you have available, the skills and preferences of your team and stakeholders, and the compatibility and interoperability of the tools. Additionally, evaluate the features and performance of the data tools, such as the data sources and formats they support, the data models and schemas they use, the data transformations and validations they offer, the data queries and functions they enable, the data visualizations and outputs they generate, and the data security and governance they provide. Then compare different data tools based on these criteria to determine which ones best fit your data architecture requirements and objectives.
In my experience, it's also crucial to consider the longevity and community support behind the data tools you're evaluating. Selecting tools that have a strong community backing ensures a stream of updates, robust troubleshooting resources, and a pool of talent familiar with the tool. This can significantly reduce onboarding time, promote innovative use-cases, and offer a level of assurance that the tool won't become obsolete in the near future.
The selection of a data management tool is contingent upon various factors, including the nature and frequency of the data, organizational priorities, cost considerations, security requirements, and user-friendliness. In such evaluations, a preference for a mature tool is advisable, as it is likely to have a wealth of available resources and documentation to address potential challenges.
Once you have chosen the data tools you want to combine, you need to integrate them effectively. Depending on the type and level of interaction, there are various methods to integrate data tools. Data exchange is the simplest form, where you export data from one tool and import it into another tool using a common format such as CSV or JSON. API is a more advanced form, where you use an application programming interface (API) to connect and communicate between data tools, using a common protocol such as HTTP or REST. SDK is the most sophisticated form, where you use a software development kit (SDK) to customize and extend the functionality of data tools, using a common language such as Python or R. It's important to choose the integration method that best suits your data tools and needs, and ensure that it is reliable, secure, and efficient.
When it comes to integrating data tools effectively, it's vital to start by envisioning the "happy path" of your data flow. In other words, think about the ideal scenario where everything goes smoothly from one tool to another. This not only sets the stage for a successful integration but also helps you design a reliable and efficient process. Begin by mapping out the ideal data journey, step by step. Visualize how data will move from one tool to another, what transformations or enrichments it might undergo along the way, and how it ultimately reaches its destination. This visualization serves as your guide, outlining the optimal path for your data.
Different kind of data tools are available in market based on their purpose and use. Combining different data tools is advisable based on customer need, data size and budget. In case of large enterprises where data is huge, it is common practice to suggest automation of processes to reduce time and effort to manage data smoothly. The journey from raw data generated at source to valuable business data at final stage consist of multiple tools to support the process. Based on data types and complexity of data output, it is advisable to use best tool available in market to connect them
Once you have integrated your data tools, you need to optimize them for optimal performance and quality. The type and level of improvement you need will determine the optimization strategy you should use. Data cleansing, for example, involves using data quality tools or data integration tools to identify and correct errors, inconsistencies, and duplicates in your data. Data indexing requires the use of data integration tools or data analysis tools to create and maintain indexes for your data. Data caching is the process of storing and retrieving data in a faster and more convenient way, using data integration tools or data visualization tools. When applying optimization techniques, make sure they are effective, consistent, and maintainable.
Key capabilities that I normally look at while looking at Tools in Data landscape for better results - Current fitment in enterprise landscape - Integration capabilities - Opportunities for Customization - Security Coverage - TCO - Overlap of Features - Responsive to Changes - All of the above : Fulfillment for business use cases
Optimize data tools with organized indexing, efficient algorithms, caching common data, parallel processing, and ongoing performance monitoring for improved speed and efficiency.
To ensure that your data tools are working properly and delivering the expected results, you need to monitor them. Depending on the type and level of information you need, there are various methods to do this. Data logging is the process of recording and storing data events and activities, using data integration or data analysis tools. Data auditing is the process of reviewing and verifying data events and activities, with data quality or data governance tools. Lastly, data reporting is the process of presenting and communicating data events and activities, using data visualization or dashboard tools. Choose the monitoring methods that best suit your data tools and needs, making sure that they are timely, relevant, and actionable.
I do not agree that data reporting belongs to a bullet about monitoring data tools. Reporting happens, and then monitoring. Whether it is a report or a visualization, you will always need to be monitoring it. For important dashboards, I often make a separate validation dashboard to help with monitoring. Tableau has helpful data driven alerts, which can help you passively monitor the data.
I consistently analyze data in Excel, documenting key logic points. Subsequently, I examine the data flow and contemplate data modeling in reporting tools. I prefer to follow a waterfall model, focusing on channeling data to a central point, and then create a demo report for analysis based on my business logic to monitor it effectively.