Modern applications don’t function in isolation. To get the most out of the enterprise apps you build or buy, you’ll have to connect them to other applications. In other words, data engineers have to engage in effective application integration to achieve their business goals. Sometimes, this means connecting one application directly to another. But this is a rare occurrence in digitally transformed industries.
From system logs to web scraping, there are many good reasons why you might have extremely large numbers of small data files at hand. But how can you efficiently process and analyze these files to uncover the hidden insights that they contain? You might think that you could process these small data files using a solution like Apache Hadoop, which has been specifically designed for handling large datasets.
In this last installment, we’ll discuss a demo application that uses PySpark.ML to make a classification model based off of training data stored in both Cloudera’s Operational Database (powered by Apache HBase) and Apache HDFS. Afterwards, this model is then scored and served through a simple Web Application. For more context, this demo is based on concepts discussed in this blog post How to deploy ML models to production.
Digital transformation is a hot topic for all markets and industries as it’s delivering value with explosive growth rates. Consider that Manufacturing’s Industry Internet of Things (IIOT) was valued at $161b with an impressive 25% growth rate, the Connected Car market will be valued at $225b by 2027 with a 17% growth rate, or that in the first three months of 2020, retailers realized ten years of digital sales penetration in just three months.
In the previous posts in this series, we have discussed Kerberos and LDAP authentication for Kafka. In this post, we will look into how to configure a Kafka cluster to use a PAM backend instead of an LDAP one. The examples shown here will highlight the authentication-related properties in bold font to differentiate them from other required security properties, as in the example below. TLS is assumed to be enabled for the Apache Kafka cluster, as it should be for every secure cluster.
The wait is nearly over, and soon we’ll all be privy to this year’s Gartner Magic Quadrant for Analytics and BI Platforms. Qlik is proud of its 15-year history and ranking as a leader for the last decade in this signature research, and we are enthusiastic about sharing a complimentary copy of the full report when it publishes at this location: https://www.qlik.com/us/gartner-magic-quadrant-2021
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