ETL Tools SQL Structured Data Unstructured Data 
Knowledge Hut
JANUARY 30, 2024
Goal To extract and transform data from its raw form into a structured format for analysis. To uncover hidden knowledge and meaningful patterns in data for decision-making. Data Source Typically starts with unprocessed or poorly structured data sources. Analyzing and deriving valuable insights from data.
Knowledge Hut
DECEMBER 26, 2023
Certain roles like Data Scientists require a good knowledge of coding compared to other roles. Data Science also requires applying Machine Learning algorithms, which is why some knowledge of programming languages like Python, SQL, R, Java, or C/C++ is also required.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Knowledge Hut
NOVEMBER 3, 2023
MongoDB is a NoSQL database that’s been making rounds in the data science community. MongoDB’s unique architecture and features have secured it a place uniquely in data scientists’ toolboxes globally. Let us see where MongoDB for Data Science can help you. How are MongoDB and Data Science Shaping the Future?
Ascend.io
AUGUST 31, 2023
For example, unlike traditional platforms with set schemas, data lakes adapt to frequently changing data structures at points where the data is loaded , accessed, and used. ELT The ETL to ELT to EtLT Evolution For many years, data warehouses with ETL and data lakes with ELT have evolved in parallel worlds.
AltexSoft
AUGUST 29, 2023
Data sources can be broadly classified into three categories. Structured data sources. These are the most organized forms of data, often originating from relational databases and tables where the structure is clearly defined. Semi-structured data sources. Unstructured data sources.
ProjectPro
DECEMBER 7, 2021
It can also consist of simple or advanced processes like ETL (Extract, Transform and Load) or handle training datasets in machine learning applications. In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. Step 1- Automating the Lakehouse's data intake.
ProjectPro
OCTOBER 15, 2014
Makes use of exact variation of dedicated SQL DDL language by defining tables beforehand. Pig is SQL like but varies to a great extent. Directly leverages SQL and is easy to learn for database experts. Hive Query language (HiveQL) suits the specific demands of analytics meanwhile PIG supports huge data operation.
Expert insights. Personalized for you.
52 
Let's personalize your content