Xavient - DiP
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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.
Xavient - DiP
- 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 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