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The Quick and Easy Way to Analyze Numpy Arrays

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The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par
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MemSQL in Advanced Data Analytics

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Why use a battery of "complicated" and "immature" tools like Kafka, Zookeeper, and NoSQL databases to support low-latency big data applications when you can use a durable, consistent, SQL-compliant in-memory database?

 This is the question NewSQL in-memory database vendors MemSQL and VoltDB are posing to big-data developers who are trying to build real-time applications. MemSQL this week announced a two-way, high-performance MemSQL Spark Connector designed to complement the fast-growing Apache Spark in-memory analytics platform.  

 "There's a lot of excitement about Spark, but many data scientists struggle with complexity and the high degree of expertise to work with related data pipelines," said Erik Frenkiel, CEO and cofounder of MemSQL, in a phone interview with InformationWeek. "As a database, MemSQL offers durability and transaction support, so it can simplify those real-time data pipelines, providing the ability to ingest data and query the system through a SQL interface."

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