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Blind 75 LeetCode Questions - LeetCode Discuss Data Manipulation and Analysis Proficiency in working with data is crucial. This includes skills in data cleaning, preprocessing, transformation, and exploratory dataanalysis (EDA).
Key Takeaways Big Data originates from diverse sources, including IoT and social media. Data lakes and cloud storage provide scalable solutions for large datasets. Processing frameworks like Hadoop enable efficient dataanalysis across clusters. Data Lakes allows for flexibility in handling different data types.
Data Warehousing A data warehouse is a centralised repository that stores large volumes of structured and unstructured data from various sources. It enables reporting and DataAnalysis and provides a historical data record that can be used for decision-making.
Key Takeaways Big Data originates from diverse sources, including IoT and social media. Data lakes and cloud storage provide scalable solutions for large datasets. Processing frameworks like Hadoop enable efficient dataanalysis across clusters. Data Lakes allows for flexibility in handling different data types.
At the core of Data Science lies the art of transforming raw data into actionable information that can guide strategic decisions. Role of Data Scientists Data Scientists are the architects of dataanalysis. They clean and preprocess the data to remove inconsistencies and ensure its quality.
They enable flexible data storage and retrieval for diverse use cases, making them highly scalable for big data applications. Popular data lake solutions include Amazon S3 , AzureData Lake , and Hadoop. Data Processing Tools These tools are essential for handling large volumes of unstructured data.
Navigate through 6 Popular Python Libraries for Data Science R R is another important language, particularly valued in statistics and dataanalysis, making it useful for AI applications that require intensive data processing. Python’s versatility allows AI engineers to develop prototypes quickly and scale them with ease.
Best Big Data Tools Popular tools such as ApacheHadoop, Apache Spark, Apache Kafka, and Apache Storm enable businesses to store, process, and analyse data efficiently. Key Features : Integration with Microsoft Services : Seamlessly integrates with other Azure services like AzureData Lake Storage.
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