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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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5 Tricky Python Interpreter Interview Questions

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Python is an interpreter, which converts to byte-code when it first encounters the .py module. It is portable. And, it is a one-time job. IN THIS PAGE Python Interpreter Interview Questions Python Interpreter 1 - What's an Interpreter? Python interpreter  converts source-code to byte-code when you first execute it is non-runnable code. The only software can process that code. During Python installation, a virtual environment will create. This environment can parse the byte code. 2 - What're the differences Between Bytecode and Machine Code? Machine code is binary that you can see in the form of 0,1s. Byte code is non-readable, only a designated software can parse it. 3 - How does Python Interpreter Work? Self-read flow chart. The smiley is byte-cod e. It uses other libraries and create byte-code. Execution flow of Interpreter 4 - When you Install Python, the list of components do you think to install? Interpreter and Libraries. Interpreter. The process of the interpreter is Bla...
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Top Hadoop Architecture Interview Questions

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The hadoop.apache.org web site defines Hadoop as "a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models." Quite simply, that's the philosophy: to provide a framework that's simple to use, can be scaled easily, and provides fault tolerance and high availability for production usage. The idea is to use existing low-cost hardware to build a powerful system that can process petabytes of data very efficiently and quickly. More : Top selected Hadoop Interview Questions Hadoop achieves this by storing the data locally on its DataNodes and processing it locally as well. All this is managed efficiently by the NameNode, which is the brain of the Hadoop system. All client applications read/write data through NameNode. Hadoop has two main components: the Hadoop Distributed File System (HDFS) and a framework for processing large amounts of data in parallel using the MapReduce paradigm HDFS ...
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Data analysis report these are example queries to use on final data

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ApplyAnalytics@twitter The role of data analysis will come into picture, once you have cleaned and filter the raw unstructured data. The next stage is called analysis. Your success of data analysis project is based preparing highly informative final report. Tip:  What could you investigate with data To prepare analysis report, you need to ask some intelligent questions. These are example questions you can use. Based on your questions, you  need to prepare SQL queries to get the desired report or dashboard from your final data or cleaned data.  The report or dashboard should be such that it should improve client business. Let us use some case study on world bank data, what are the questions come into mind:  How much (in USD) is spent on healthcare in total in each country?  How much (in USD) is spent per capita in each country?  In which country is the most spent per person?  In which country is the least spe...
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