Data Professionals' Perspectives on Working with Various Data Sources

-

  Data Professionals' Perspectives on Working with Various Data Sources

Data professionals shared insights into their experiences with diverse data sources. Here's a summary of their perspectives:

  1. Relational Databases:

    • SQL is a powerful tool for moving, structuring, and securing data.
    • Challenges arise in data versioning, especially when migrating between vendors.
    • Flexibility and openness to new solutions are key when consistently moving data.

  2. Evolution Beyond Relational Databases:

    • Relational databases are reliable but faced scrutiny with the rise of unstructured data (logs, documents, XML, JSON).
    • Google BigTable's architectural model inspired databases like Cassandra and HBase, addressing challenges in heavy write-intensive applications.
    • Data engineers must adapt to work with various data formats, including proprietary ones.

  3. Diverse Data Formats:

    • Log data is challenging due to its unstructured nature; custom tools may be required.
    • XML faced resource-intensive issues, leading to the popularity of JSON in RESTful APIs.
    • Newer formats like Apache Avro gain popularity for their data storage efficiency.

  4. Challenges in Data Conversion:

    • Converting data between different databases (e.g., Db2 to SQL Server) can be challenging.
    • Dealing with various characters in the data requires thoughtful consideration of delimiters.
    • Tables with different special characters may need unique separators, adding complexity to data conversion projects.

  5. Adaptability and Continuous Learning:

    • Data professionals must adapt to working with standard and proprietary formats, relational databases, NoSQL, and big data repositories.
    • Skills may need to be acquired on the go to handle different datasets, formats, and sources.

Working with data is a dynamic journey that demands flexibility, adaptability, and a continuous learning mindset.

Comments

Post a Comment

Popular posts from this blog

Lila's Journey to Becoming a Data Scientist: Her Working Approach on the First Task

-
  Education and Skill Acquisition: Lila, with an economics background, decides to transition to data science. Enrolls in the IBM Data Science Professional Certificate for comprehensive training. Gains proficiency in statistics, machine learning, and programming languages. Building a Strong Foundation: Develops a deep understanding of data manipulation and visualization using Python libraries. Visualization for Storytelling: Learns to create informative visualizations, aiding in effective communication of findings. Hands-On Experience: Engages in Kaggle competitions and personal data projects for practical experience. Establishes a GitHub profile to showcase her projects and skills. Data Wrangling and Preprocessing: Focuses on data cleaning, preprocessing, and handling missing data and outliers. Communication and Storytelling: Hones data storytelling skills using tools like Matplotlib and Plotly. Networking and Collaboration: Actively participates in data science communities, attend...
Read more

Notes on Hiring for Data Science Teams

-
  Unicorn Myth in Hiring: Companies often seek individuals with a myriad of skills for data science roles. Ideal candidates possess domain-specific knowledge, excel in data analysis, and have strong presentation and storytelling skills. However, finding such a "unicorn" is rare, and the focus should shift to other essential qualities. Resonance with Company DNA: The key is to identify applicants who resonate with the company's values and business domain. While analytics skills can be taught, passion for the business is intrinsic and contributes significantly to productivity. Curiosity Matters: Curiosity is a top priority in hiring for a data science team. Candidates should demonstrate a broad curiosity, showing interest in various aspects of their surroundings beyond just data science. Sense of Humor: A lighthearted approach is crucial, as being too serious may hinder collaboration and creativity. A sense of humor aids in navigating challenges and fosters a positive work ...
Read more

switch functions

-
  Nested IF Function : The  nested IF  function allows you to perform tests with multiple conditions and return different values based on the results of those tests. It is called “nested” because one IF function is embedded inside another IF function. Syntax of a generic nested IF formula: =IF(condition1, result1, IF(condition2, result2, IF(condition3, result3, result4) ) ) Each subsequent IF function is embedded into the  value_if_false  argument of the previous function. SWITCH Function : The  SWITCH  function is a more compact alternative to nested IF statements. It compares an expression against a list of values and returns the result according to the first matching value. Syntax of the SWITCH function: =SWITCH(expression, value1, result1, [default or value2, result2], ... [default or valueN, resultN]) It has four arguments: expression : The value to compare against the list of values. valueN : A value com...
Read more