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Big data platforms such as ApacheHadoop and Spark help handle massive datasets efficiently. Techniques like NaturalLanguageProcessing (NLP) and computer vision are applied to extract insights from text and images. Together, these tools enable Data Scientists to tackle a broad spectrum of challenges.
Check out this course to build your skillset in Seaborn — [link] Big Data Technologies Familiarity with big data technologies like ApacheHadoop, Apache Spark, or distributed computing frameworks is becoming increasingly important as the volume and complexity of data continue to grow.
Cloud Storage: Services like Amazon S3, Google Cloud Storage, and Microsoft Azure Blob Storage provide scalable storage solutions that can accommodate massive datasets with ease. Data Processing Frameworks Processing frameworks are essential for analysing large datasets efficiently.
Cloud Storage: Services like Amazon S3, Google Cloud Storage, and Microsoft Azure Blob Storage provide scalable storage solutions that can accommodate massive datasets with ease. Data Processing Frameworks Processing frameworks are essential for analysing large datasets efficiently.
Popular data lake solutions include Amazon S3 , Azure Data Lake , and Hadoop. Data Processing Tools These tools are essential for handling large volumes of unstructured data. They assist in efficiently managing and processing data from multiple sources, ensuring smooth integration and analysis across diverse formats.
Java is also widely used in big data technologies, supported by powerful Java-based tools like ApacheHadoop and Spark, which are essential for data processing in AI. NaturalLanguageProcessing (NLP) NLP involves programming computers to process and analyze large amounts of naturallanguage data.
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