[GRIFFIN-326] New Data Connector for Elasticsearch

**What changes were proposed in this pull request?**

This ticket proposes the following changes,
- Deprecate the current implementation in favour of the direct implementation in the official [elasticsearch-hadoop](https://github.com/elastic/elasticsearch-hadoop/tree/master/spark/sql-20) library.
- This library is built on DataSource API built on spark 2.2.x+ and thus brings support for filter pushdowns, column pruning, unified read and write and additional optimizations.
- Many configuration options are available for ES connectivity, [check here](https://github.com/elastic/elasticsearch-hadoop/blob/master/mr/src/main/java/org/elasticsearch/hadoop/cfg/ConfigurationOptions.java).
- Any filters can be applied as expressions directly on the data frame and are pushed automatically to the source.

**Does this PR introduce any user-facing change?**
Yes. As mentioned above, the old connector has been deprecated and config structure for Elasticsearch data connector has changed now.

**How was this patch tested?**
Griffin test suite and additional unit test cases

Author: chitralverma <chitralverma@gmail.com>

Closes #569 from chitralverma/new-elastic-search-connector.
32 files changed
tree: a23a5d2f3aeb4a01595da282e4816ed26d0ea938
  1. griffin-doc/
  2. measure/
  3. service/
  4. ui/
  5. .gitignore
  6. .scalafmt.conf
  7. .travis.yml
  9. KEYS
  11. merge_pr.py
  12. NOTICE
  13. pom.xml
  14. README.md
  15. scalastyle-config.xml

Apache Griffin

Build Status License: Apache 2.0

The data quality (DQ) is a key criteria for many data consumers like IoT, machine learning etc., however, there is no standard agreement on how to determine “good” data. Apache Griffin is a model-driven data quality service platform where you can examine your data on-demand. It provides a standard process to define data quality measures, executions and reports, allowing those examinations across multiple data systems. When you don't trust your data, or concern that poorly controlled data can negatively impact critical decision, you can utilize Apache Griffin to ensure data quality.

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