The document introduces the Data Ingestion Platform (DIP), which facilitates real-time or batch data ingestion from various sources into big data systems, emphasizing the need for automation and efficiency in data processing. It highlights features such as support for multiple data formats, an intuitive user interface, and integration with technologies like Apache Kafka and Apache Spark. Additionally, the document outlines use cases for sentiment analysis and log analysis, showcasing Xavient's expertise in delivering scalable solutions.

1. Data Ingestion Platform (DiP)
Co-Dev opportunity to ingest any data in near
real time
www.xavient.com

2. www.xavient.comXavient Data Ingestion Platform (DiP)
Introduction
When numerous big data sources exist in diverse
formats (the sources may often number in the
hundreds and the formats in the dozens), it can
be challenging for businesses to ingest data at a
reasonable speed and process it efficiently in
order to maintain a competitive advantage. To
that end, vendors offer software programs that
are tailored to specific computing environments
or software applications.
When data ingestion is automated, the software
used to carry out the process may also include
data preparation features to structure and
organize data so it can be analyzed on the fly or
at a later time by business intelligence (BI) and
business analytics (BA) programs.
Data Ingestion Platform (DiP) is a system to
ingest data into Big Data systems. Data can be
streamed in real time or ingested in batches.
When data is ingested in real time, each data item
is imported as it is emitted by the source. When
data is ingested in batches, data items are
imported in discrete chunks at periodic intervals
of time. An effective data ingestion process
begins by prioritizing data sources, validating
individual files and routing data items to the
correct destination.
* This is a co-dev opportunity and provides initial baselines and
access to Big Data experts to enhance it further to meet the business
requirements
“Every business is an
analytics business, every
business process is an
analytics process, and every
business user is an analytics
user”
- Gartner
Challenges Faced
Business want to get data from various sources into
Hadoop or NoSql databases for faster access in near real
time. There is need for a platform that can help to build
a scalable and fault tolerant data pipeline.
This system should allow to run the following:
High Speed
Filtering and
Pattern Matching
Contextual
Enrichment
on the Fly
Real-time KPIs,
Analytics, Baselining
and Notification
Predictive
Analytics
Actions and
Decisions
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2 |

3. www.xavient.com Xavient Data Ingestion Platform (DiP)3 |
Data Ingestion Platform (DiP)
Real time data ingestion using Data Ingestion Platform
(DiP) harness the powers of Apache Apex, Apache Flink,
Apache Spark and Apache Storm to stream data into
lambda architecture. Apache Kafka plays a key role as
messaging bus from source to streaming component.
DiP comes along with a UI in case users wants to upload
data from their desktops and also, any data can be
ingested from any source like Cloud Storage or local file
system. UI plays a key role in learning and choosing the
streaming components in the initial phase of
understanding the system.
DiP Technology Stack
• Source System – Web Client
• Messaging System – Apache Kafka
• Target System – HDFS, Apache HBase, Apache Hive
• Reporting System – Apache Phoenix(CLI), Apache
Zeppelin
• Streaming API’s – Apache Apex, Apache Flink,
Apache Spark and Apache Storm
• Programming Language – Java
• IDE – Eclipse
• Build tool – Apache Maven
• Operating System – CentOS 7
DiP Features
Any data source
Any data type
Easy to use UI
Data Visualization
High Level API’s
Java, Scala, Client
bindings
Architecture
• Flume / Client UI ingests data to Kafka Queues
• Platform picks data from subscribed Kafka topics
• Four streaming APIs : Apex Streaming, Flink Streaming, Spark Streaming, Storm Streaming
(Windowed Aggregations to MySQL)
• Process it in real time or micro-batching : HBase, HDFS (External tables on Hive tables), Phoenix
views on Zeppelin
G
U
I
XML
JSON
CSV
TXT
K
A
F
K
A
B
R
O
K
E
R
HBASE
HDFS
Hive
External
tables
Phoenix
Reporting
Zeppelin
Kafka
Operator
Classifier
Operator
File
Operator
HBase
Operator
Apex Streaming
Kafka
Source
Map
Data
HDFS
Sink
HBase
Sink
Flink Streaming
Kafka
Stream
Spark Streaming
Spark
Executers
Kafka
Spout
Storm Topology HDFS
bolt
HBASE
bolt
Filter
bolt
Data Ingestion Platform

4. www.xavient.comXavient Data Ingestion Platform (DiP)4 |
DiP comes with an easy to use UI that offers the following features –
• Switch easily between the supported streaming engines just by clicking on a radio button.
• Supports xml, json and tsv data formats
• Use text area to enter data manually for getting processed
• Process files for batch processing by simply uploading them
DiP User Interface (Co-Dev)
Use Cases
Sentiment
Analysis
Click
Stream
Analysis
Log
Analysis
Social
Media and
Customer
Sentiment
Analyze
Machine
and Sensor
Data

5. www.xavient.com Xavient Data Ingestion Platform (DiP)5 |
Great Ideas… Simple Solutions is what Xavient thrives on. As a global IT consulting
and software services company, we focus on transforming business ideas into
effective solutions.
Founded in 2002, the company is led by a passionate team of experts who come with
a history of entrepreneurial and management success. Xavient is headquartered in
the U.S with an international network of delivery centers primarily established in
India.
About Xavient
• Enabled one of the largest Billing
Transformation initiative in North America
• Powered one of the largest OTT platform for
video-on-demand services
• Designed one of the most engaging high
touch - high performance Retail UI/UX
• Proven expertise & unflinching focus on Digital
Media & Communication space for over 14
years
• Partner of choice for 4 out of Top 5 CSPs in the
US
• Developed the Live Streaming solution for a
Weather channel supporting next generation
internet connected devices