What tools should you master for big data analytics certification?

Surely one should have good hands on Hadoop when it comes to distributed storage and processing of large data sets as in for fast in-memory data processing Apace Spark is more popular and one should build small projects or contribute to open-source projects to deepen understanding and proficiency with these tools.

1) Learn SQL and Python at basic level 2) Learn distributing processing. Learn one distributed framework , start with mapreduce to understand concept and then learn spark ( unified distributing processing framework ) that can work for batch, streaming, ML and lakehouse queries. Its widely accepted and faster compared to map reduce. Databricks community version is best to practice spark as its convenient and no hassles of local setup. You can also use EMR/Glue on AWS. Google dataproc is also an option. 3) Learn orchestration framework like open source Apache airflow. Azure data factory if in case you are using azure cloud

To get certified in big data analytics, focus on mastering these tools: 1. Python and R for data analysis. 2. SQL for database queries. 3. Hadoop and Spark for big data processing. 4. Tableau and Power BI for data visualization. 5. Scikit-Learn, TensorFlow, and PyTorch for machine learning. 6. Cloud platforms like AWS, Google Cloud, and Azure for scalable data solutions. These tools will help you handle and analyze big data effectively.

1) Get a basic understanding of Python and SQL. 2) Acquire knowledge about distributed processing. To gain an understanding of the notion, start with learning mapreduce. After that, move on to learning spark, which is a unified distributed processing framework that can be used for batch, streaming, machine learning, and lakehouse queries. In comparison to map reduction, it is faster and more universally accepted. Because it is convenient and requires no difficulties with local setup, the Databricks community version is the best for practicing Spark. EMR/Glue is also available on AWS. Another possibility is Google Dataproc. 3) Get familiar with open-source orchestration frameworks like Apache Airflow.

The Hadoop ecosystem is a cornerstone of big data processing and is essential for anyone serious about a career in data analytics. This open-source framework allows for the distributed processing of large data sets across clusters of computers using simple programming models. Hadoop’s ecosystem includes a range of tools like Hadoop Common, HDFS, YARN, and MapReduce, which handle various aspects of data storage, resource management, and processing. Additionally, components such as Apache Hive, Pig, HBase, and Spark enhance data querying, scripting, and real-time processing capabilities. Mastering these tools enables professionals to efficiently manage, process, and analyze vast amounts of data.

SQL is the most valuable thing you can learn as a data analyst. Most of my time is spent pulling data out in SQL into excel or using SQL to double check reports in a BI tool

Knowledge of fundamental querying and managing relational databases SQL seems to more helpful while running through NoSQL Databases: Such as MongoDB, Cassandra, and HBase will be helpful for handling unstructured data this Both are enough to carry out

SQL remains your relational database workhorse, perfect for structured data. But the data world is expanding! Unstructured data is exploding, and that's where NoSQL comes in. Unlike the rigid tables of SQL, NoSQL offers flexibility, letting you store information in a variety of formats. Think of it like this: SQL is your filing cabinet for neatly organized documents, while NoSQL is your digital warehouse for all kinds of data, from emails to social media posts. By mastering both, you'll be a data Swiss Army Knife, able to handle any data management challenge big data throws your way. This combined expertise will be crucial for your big data certification.

Structure Query Language or SQL is the foundation language for any database development. This is widely used in all relational databases for data handling. This is very powerful and the basics of RDBMS. We must know SQL for big data analysis. Cloud data warehouse Snowflake also uses SQL. this is an MPP or Massive Parallel Processing data warehouse database. You have to be very expert in SQL query writing for any Data warehousing or big data analysis. We know the NoSQL database is for unstructured data storage. It is not a relational database. Mostly it stores data in JSON format. This is very fast and big data. It doesn't need SQL for data management. But still, this is not as popular as SQL language. So we can learn this as well with SQL

data warehouses provide us generalized and consolidated data in multidimensional view. Along with generalized and consolidated view of data, Data warehouses also provide us Online Analytical Processing OLAP tools which helps us in interactive and effective analysis of data in a multidimensional space. This analysis gives a clearer picture in data generalization and data mining.

To analyze data effectively, it is key that you know data warehousing aspects, especially how tables relate to each other. Not knowing these relationships can lead you to an incorrect analysis or cause your analysis to be misinterpreted.

The key lies in understanding how these warehouses structure vast amounts of data for analysis. Specialized data warehousing tools can be your secret weapon, transforming chaotic information into a goldmine of insights. By mastering how to leverage these tools for data organization and retrieval, you'll be able to unlock the true potential of big data, uncovering actionable insights that drive real-world results. Remember, aced data warehousing skills are the foundation for big data mastery, giving you the edge in the ever-evolving field of data analytics.

The data warehouse is the data repository of de-normalized data. Traditionally Its sources are different OLTP application databases. Historical data is stored on data warehouse databases. We must know data warehouses for data analytics, data analysis, data mining, and data science. The data warehouse consists of a dimension model using fact and dimension tables. This is the source of any big data analysis, also, for data science. We need to understand the data warehouse structure, sources of data, mapping, and ETL process. Sometimes this is very complex and complicated. We need to understand and learn the insight of the data. Otherwise, we can't analyze this.

It's about understanding the "why" behind the "how." While tools like classification algorithms help you categorize data, and clustering helps group similar data points, the true magic lies in applying these techniques to unearth hidden patterns in massive datasets. Think of data mining tools as your shovel and pickaxe for uncovering buried treasure. By understanding which algorithms work best for different scenarios, you'll be able to efficiently sift through big data, extract valuable insights, and ultimately translate them into actionable business decisions. This not only equips you to ace your certification but also positions you as a data mining whiz who can unlock the true potential of big data.

Data mining is searching for patterns from historical data. The historical data can be found in the data warehouse. We can learn these skills. Data mining is required for forecasting and prediction for any organization. There are several tools available in the market for data mining. Python this the best tool for this. Also, we need to know statistics and machine learning for modern data mining. Python has a wide range of machine-learning libraries and packages. We can easily use them and create a machine-learning model. This is a modern way data mining approach. We can do this using SQL as well. But Python will be much better for data mining.

Supervised Learning: This is where your data "teaches" your model. Imagine showing it pictures of cats and dogs, helping it learn to identify them on its own. Perfect for tasks like customer churn prediction or spam filtering. Unsupervised Learning: Here, your model finds patterns in your data without any predefined categories. Think of it as letting your data reveal hidden structures, like grouping customers with similar buying habits. Reinforcement Learning: This is where your model learns through trial and error, like an AI playing a game. It gets rewarded for good decisions and penalized for bad ones, constantly refining its approach. Imagine optimizing delivery routes or personalizing recommendations based on past user interactions.

When diving into the realm of big data analytics, grasping visualization tools is like wielding a powerful sword. As you journey toward certification in this field, honing your skills with key visualization tools becomes essential. These tools serve as your window into the vast expanse of data, allowing you to transform intricate datasets into digestible visuals. Whether it's Tableau, Power BI, or matplotlib, mastering these tools isn't just about simplifying analysis—it's about amplifying your ability to convey insights with precision and impact. With mastery of visualization tools, you're equipped to navigate the complexities of big data analytics with finesse.

When you analyze data you need to have a way to visualize your findings that makes it easy to find insights and take action. Mastering a visualization is key for this. The top 2 visualization tools out there are Tableau and Power BI. As a person who has used Tableau most of his career, I can say it is easy to master. I have been using it for 13 years and I learned it initially in a days time just by playing around in the tool. Tableau also has great training as well. The UI is very intuitive and easy to use.