What are the steps for choosing the best data manipulation tool?

I think it depends on what stage of growth the company is at. If you are an early stage start up, starved for resources and your data volumes are reasonable, I would pick something simplistic either open source or off the shelf SaaS product rather than attempt to build something on your own. For more mature or bigger companies, there's not going to be a one size fits all solve. You will need to look at it from the perspective of user personas (DE vs Data Analyst vs Data Science vs ML Practitioners). You could stand up a data platform that's a collection of tooling customized by user persona. This could be a combination of off the shelf and open sourceproduct, (Databricks vs Snowflake or Redshift for storage/compute perspective),BI tooling

That's true ?? Accessing your data and understanding is the most important thing before doing any manipulation on it.???? Whether you use SQL or Python understanding the data is quite useful.???? Only this way you can get the process of extracting data effectively and performing the required transformation.? Python provides different libraries like pandas , numpy , scrapy etc to deal with any kind of data in easier way. ?? On the other hand SQL is a language which every Data Engineer know from the beginning. ?????? Data itself tells it's story if you are ready to listen. ??

Choosing the best data manipulation tool involves a systematic approach considering various factors to align with specific business needs and technical requirements. Research and compile a list of data manipulation tools that align with your requirements. Consider both open-source and commercial options. Look into tools like SQL-based databases, NoSQL databases, ETL (Extract, Transform, Load) tools, data preparation platforms, and programming languages like Python or R.

Evaluate the expertise of your team. Some tools might require specific programming skills (e.g., Python, R), while others provide more user-friendly interfaces. Ensure compatibility with existing systems and workflows. Research available tools: Explore various data manipulation tools such as Excel, Python libraries (Pandas, NumPy), R programming, SQL databases, SAS, MATLAB, etc. Assess the features, functionalities, ease of use, scalability, supported data formats, and integration capabilities of each tool.

Having a good understanding of the characteristics of a database is crucial when it comes to selecting the appropriate tool to gain insights. However, at times, we might be restricted to using specific database tools. In such situations, it is important to identify a database that complements the system you are using. This will help streamline the entire process and make it easier to manipulate data, ultimately leading to more accurate results.

I found this section of the article to be the most helpful. I think the tool really depends on what you are trying to do. If there is a point and click option that can save time like Excel or Retool, then that’s fine. If more complex logic is needed then a custom script in Python or another language is the answer. Most projects I work on, even smaller ones, often require enough complex logic that Python becomes needed. Especially nowadays when everyone wants more sophisticated calculations and analyses. I can definitely think of recent projects where Excel worked fine, but I think that is less common in my experience.

Understand your data manipulation needs. Are you working with large datasets, real-time data, structured or unstructured data? Identify the specific tasks you need the tool to perform (e.g., cleaning, transforming, analyzing, visualizing data). Rank the importance of each factor based on your specific needs.Understand what you aim to achieve with the data manipulation tool. Consider performing basic data cleaning, complex analytics, statistical analysis, visualization etc.

Define Requirements and Objectives: Clearly outline the specific goals and objectives of data manipulation. Identify the types of data to be processed, such as structured, semi-structured, or unstructured data. Determine the scale of data manipulation, considering the volume, velocity, and variety of data.

Defining the size of a database and determining how much of that data is necessary to achieve your objectives is crucial, as databases are usually large and unstructured. Having clear and attainable objectives with specific criteria can be helpful.

Assess the total cost of ownership, including licensing fees, subscription costs, maintenance, support, and any additional expenses. Consider both initial and long-term costs associated with the tool.

Understand Data Sources and Formats: Assess the variety of data sources, including databases, spreadsheets, APIs, and other data storage systems. Consider the formats of data, such as CSV, JSON, XML, or proprietary formats, to ensure compatibility with the chosen tool.

It's also vital to have a reliable and constant source of data for any data project. The quality and consistency of your data source can significantly impact the outcomes of your analysis and decision-making processes. Moreover, pre-processing your data by adjusting or filtering it before feeding it into your system is crucial. This step ensures that the data is clean, relevant, and formatted correctly, which can greatly enhance the efficiency and accuracy of your data manipulation tools and the insights they provide. Essentially, the quality of your input data can be just as important as the tools and resources you choose for your data project.

Before making a final decision, conduct a proof of concept or trial period with shortlisted tools. Test their functionalities with real or sample data to assess how well they meet your requirements.

Evaluate the tool's user-friendliness, availability of tutorials, documentation, and community support. Consider your team's expertise and the learning curve involved in adopting a new tool. Determine if the tool is within your budget. Consider factors like licensing models (one-time purchase, subscription-based), additional costs for support, updates, or additional features.

Collect feedback from stakeholders and users who participated in the testing phase. Based on all the gathered information, make an informed decision on the best data manipulation tool for your organization.

In my view, the order of steps in this article is wrong. I would rather first set my objectives, then evaluate what I need to do to reach them, then go to the resources and data that I have, and then execute. This allows me to choose the right process, the right tools, and the right datasets to reach my goal. If I start with data only, I might be limited to what I have and can achieve, because sometimes you might need more data or other tools to reach your goal. This way of working will deliver the most value.

If possible, opt for a trial or demo version to test the tool's functionalities and suitability for your specific use case. Consider user reviews, testimonials, and experiences shared by other organizations or users. Select the tool that best fits your requirements, considering all the factors outlined above.

Selecting the most suitable data manipulation tool for your use case involves considering factors such as the nature of your data, the complexity of transformations required, and the specific functionalities offered by each tool. Popular tools like Pandas in Python are versatile for small to medium-sized datasets, while Apache Spark is well-suited for large-scale distributed processing. Tools like SQL, R, and Python libraries offer diverse capabilities. Assess your data size, processing speed needs, and the programming languages you are comfortable with to determine the best fit. Additionally, consider the integration capabilities with your existing tech stack and the scalability of the tool for future growth.

Security and Compliance: Prioritize data security by assessing the tool's encryption capabilities, access controls, and compliance with industry regulations. Consider tools that support auditing and logging features for tracking data manipulation activities.