Top 10 database analytics and tools

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Top 10 Database Analytics and Tools
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• Apache Spark
• No doubt, Apache Spark is still in demand and
  version 2.2 was released in the month of July
  which offered a large number of excellent
  features to the core, enhancements to the Kafka
  streaming interface, and extra algorithms in
  GraphX and Mllib. Support of distributed machine
  is done by SparkR and it also sees lots of
  improvements, particularly in the SQL integration
  area.
• Apache Solr
• Lucene Index technology is used to build it is
  the distributed document/index database that 
• would, could and does. Solr is the best thing for
  you to handle simple or complex documents. It is
  Solr's strength to find things in a mountain of
  text to do more along with the ability to execute
  SQL graph queries. There are new point types
  developed and continued does execute lots of
  queries.

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• Apache Arrow
• For increasing the speed of big data, a high
  speed, cross-system data layer, columnar called
  Apache Arrow is used. With the help of Arrow data
  is stored in memory and the serialization or
  deserialization steps which are costlier can be
  omitted as it creates lots of problems. There are
  lots of Apache big data projects involving
  developers like Parquet, Cassandra, Spark, Kudu,
  and Storm which will be processed by Apache Arrow
  project.
• Apache Kudu
• To become a prime component of big data
  architecture, Apache Kudu is the best choice.
  Large amounts of data require frequent updates
  and there is a need for a timely basis of
  analytics and for such scenarios Kudu is
  optimized. Traditional Apache Hadoop architecture
  is a challenge and it normally leads to complex
  HDFS and HBase solutions and it is quite
  challenging. There are easier and good
  architectures like IOT, streaming machine
  learning processing, and time series is promised
  by Kudu.

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• Apache Zeppelin
• Most of the analysts, developers, data scientists
  consider Apache Zeppelin as a Rosetta Stone. For
  pulling from a slew of interpreters there are
  various data stores and analyze in multiple
  languages. Apache Solr index is used for pulling
  data from Oracle database and cross-reference.
  Your data frame can be analyzed in R by your
  statistician before favorite python library is
  used by the data scientists. 
• R Project
• Little introduction is required by R programming
  language and in the year 2017 support for
  Microsoft grows with Oracle and IBM along with
  smaller players. There are lots of statistical
  computing algorithm of importance comprised in
  the CRAN Comprehensive R Archive Network which is
  run along with adequate graphics.

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• Apache Kafka
• For building real-time data pipelines and
  streaming apps, Apache Kafka is a shared
  streaming platform that is used. It is rapid,
  fault-tolerant, scalable, available in thousands
  of companies. A stream of records is published
  and subscribed with the help of Kafka. In an
  error-free way, you can store data using this.
• Cruise Control
• It is difficult to manage Kafka otherwise it is a
  powerful and stable distributed streaming
  platform. Although there is no manual power
  required for handling errors it is quite
  imbalanced. On Kafka resource monitoring and
  re-balancing under the observation of Linked In
  SRE's are provided a lot of time. On the late
  August, it was just open sourced. 

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• Janus Graph
• On a distributed graph database Janus Graph is
  constructed with a column family database. There
  are other famous open source graph databases
  which assist large graphs. There are lots of
  features in Janus Graph which are combined with
  Apache Spark and Apache Solr. In a graph shaped
  problem, the data lend itself to a graph
  structure which is responded by JanusGraph.
• Apache TinkerPop
• All the famous graph processing frameworks are
  powered like the Neo4j, Titan, Spark, and
  TinkerPop that permits the users to model the
  problem domain like graph and check it using a
  graph traversal language. Open source
  implementations are lead by TinkerPop.
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