While traditional data analyst might be able to get away without being a full-fledged programmer, a big data analyst needs to be very comfortable with coding. One of the main reasons for this requirement is that big data is still in an evolution phase.
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Experience with relational and non -relational database systems is a must. Examples of non- relational database include – Mysql, Oracle, DB2. Examples of non-relational database include – NoSql : Hbase, HDFS, MongoDB, CouchDB, Cassandra, Teradeta, etc.
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A good understanding and familiarity with frameworks such as Apache Spark, Apache Storm, Apache Samza, Apache Flink and the classic MapReduce and Hadoop. These technologies help in Big Data processing which can be streamed to a great extent.
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To keep the analysis focused, to validate, sort, relate, evaluate the data, the most critical skill of a big data scientist is to have a good knowledge of the domain one is working on. In fact, the reason big data analysts are so much in demand is that its very rare to find resources who have a thorough understanding of technical aspects, statistics and business.
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We are living in an era whereby software product development is the order of the day. However, technological advancements have made it a trend for developers to release a quality code quickly and in a safe state. According to Google conference held in Napa at JFrog SwampUP in the year 2018, at least 500 million tests are run every single day in Google’s system.
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n the current century, a lot of mobile app development keep increasing every other day. Approximately 1.8 million applications on the Apple App Store and 2.5 million applications are on the Google Play Store. This implies that the applications are expected to keep advancing in the years to come.
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As the technological world keeps changing, the education sector keeps improving as well. Machine learning is among the advanced technology in the sector concerning education. Machine learning has uplifted the education industry to extreme heights.
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