Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

Chapter 2
ETL with Python (Structured Data)
cols = sys.stdin.readline().strip()
out.write("columns=" + cols + "\n")
out.write("source end"+"\n")
print "Enter the Data Source Type:"
print "1. MySql"
print "2. Text"
print "3. Exit"
if(int(data1) == 3):
out.close()
sys.exit()
Elasticsearch
The Elasticsearch (ES) low-level client gives a direct mapping from
Python to ES REST endpoints. One of the big advantages of Elasticsearch
is that it provides a full stack solution for data analysis in one place.
Elasticsearch is the database. It has a configurable front end called
Kibana, a data collection tool called Logstash, and an enterprise security
feature called Shield.
This example has the features called cat, cluster, indices, ingest,
nodes, snapshot, and tasks that translate to instances of CatClient,
ClusterClient, IndicesClient, CatClient, ClusterClient,
IndicesClient, IngestClient, NodesClient, SnapshotClient,
NodesClient, SnapshotClient, and TasksClient, respectively. These
instances are the only supported way to get access to these classes and
their methods.
You can specify your own connection class, which can be used by
providing the connection_class parameter.
# create connection to local host using the ThriftConnection
Es1=Elasticsearch(connection_class=ThriftConnection)
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