Big Data

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Julia vs. Python: Which is best for data science?

Python has turned into a data science and machine learning mainstay, while Julia was built from the ground up to do the job

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Cython tutorial: How to speed up Python

How to use Cython and its Python-to-C compiler to give your Python applications a rocket boost

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Is your data lake open enough? What to watch out for

Like yesterday’s data warehouses, today’s data lakes threaten to lock us into proprietary formats and systems that restrict innovation and raise costs

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Data structures and algorithms in Java: A beginner's guide

Learn all about array and list data structures in Java, and the algorithms you can use to search and sort the data they contain

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What is Apache Spark? The big data platform that crushed Hadoop

Fast, flexible, and developer-friendly, Apache Spark is the leading platform for large-scale SQL, batch processing, stream processing, and machine learning

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Neo4j 4.0 targets scalability, security, and performance

Leading native graph database adds long-awaited horizontal sharding, granular security, and reactive processing

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Why data-driven businesses need a data catalog

Enterprises need better tools to learn and collaborate around data sources. Data catalogs with pioneering machine learning capabilities can help you tap your valuable data

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Qubole review: Self-service big data analytics

Cloud-native data platform puts Spark, Presto, Hive, and Airflow at your fingertips, while controlling your cloud spending

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Who should be responsible for your data? The knowledge scientist

Organizations that recognize the importance of clean and reliable data while elevating knowledge work will move faster along the path to true data-driven decision-making

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Will data gravity favor the cloud or the edge?

An industry standard confidential computing framework could unlock secure data processing at both the center and the edge

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What is big data analytics? Fast answers from diverse data sets

Analyzing large volumes of data is only part of what makes big data analytics different from traditional data analytics

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Semi-supervised learning explained

Using a machine learning model’s own predictions on unlabeled data to add to the labeled data set sometimes improves accuracy, but not always

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How Qubole addresses Apache Spark challenges

The Qubole Data Platform brings streamlined configuration, auto-scaling, cost management, and performance optimizations to Spark-as-a-service

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Deep learning frameworks: PyTorch vs. TensorFlow

If you actually need a deep learning model, PyTorch and TensorFlow are both good choices

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PyTorch vs. TensorFlow: How to choose

If you actually need a deep learning model, PyTorch and TensorFlow are the two leading options

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10 Splunk alternatives for log analysis

Splunk may be the most famous way to make sense of mass quantities of log data, but it is far from the only player around

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10 Splunk alternatives for log analysis

Splunk may be the most famous way to make sense of mass quantities of log data, but it is far from the only player around

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Automated machine learning or AutoML explained

AutoML frameworks and services eliminate the need for skilled data scientists to build machine learning and deep learning models

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