Ticon Technologies Princeton Business Park, 3353 Bradshaw Road Sacramento, CA 95827

BIG DATA

We Do Big Data

Polar offers cutting edge open source big data Technologies-as-a-Service to your applications. The Big Data Landscape includes the Hadoop ecosystem, Cassandra, Mongo, the Spark ecosystem, Solr, Kafka and Elastic search.
Ourexpert Big Data team delivers innovative solutions to solve difficult high-volume, low-latency, analytics, business intelligence, machine learning and other problems utilizing Big Data techniques. Theteam delivers large-scale programs that integrate processes with technology to help our clients achieve high performance. We design, implement and deploy custom applications on Hadoop. Polar Big Data Services include implementation of complete Big Data solutions, data acquisition, storage, transformation, and analysis. We also design, implement and deploy ETL to load data into Hadoop.

Ticon’s Competencies in Big Data

Our expertise includes both Hadoop ecosystem to create batch processing solutions and Sparkecosystem to handle data on the move or real time data.

Ticon’s Approach

We begin by gaining an understanding of the “Vision” of how insights drawn from diverse data sets, big or small, align and contribute to organizational objectives before digging any data sources.

Big Data

Most commonly, Big Data is defined as a large set of data that is very unstructured and disorganized. Polar looks at the typical big data sources such as “Enterprise data”, “Machine/Sensor generated data” and “Social media data”.

Ticon gaina thorough understanding of seven dimensions of your data to recognize what makes your Big Data:

  • Volume
  • Velocity
  • Variety
  • Variability
  • Veracity
  • Visualization
  • Value

Data Lake

A data lake is a storage repository that holds a vast amount of unstructured data in its original format until it is needed. Bringing data into the lake is easy. But as the data lake grows and the number of files and users expands the data layer becomes unstable
There is no silver bullet that validates and organizes the data in the data lake. However, Polar has a process which can automatically and accurately ingest data from a diverse set of traditional, legacy, and big data sources into a storage system (For ex. HDFS).
Polar adopted the below approach to create and ensure maintenance of enterprise data lakes in the right way. The key ingredients of our approach are:

Big Data Processing

Polar ‘s expertise includes various data processing architectures such as “Kappa” and “Lambda”. First we assess your needs, whether you have “Batch processing”, “Real-time processing”, “On-line processing”, “Distributed processing” or any combination of these.
We thendetermine the appropriate big data processing architecture best suited to handle massive quantities of data. Our solutions attempt to balance latency, throughput and fault-tolerance and provide comprehensive and accurate views of batch data, while simultaneously providing views of online data using a real-time stream.

Big Data Search

Big data search needs to be able to search through both unstructured and structured data, running many simultaneous queries. Polar has mastered implementing open source big data search tools such as “Apache Solr”, “Apache Lucene” or “Elastic search”.
We determine the appropriate product and solution architecture to ensure the business compliance, relevance and other complex search security requirements at index time rather than at the query time.Our approach in implementation of Big Data Search solutions is to separate subsystems – content acquisition, content processing and indexing -with clearly defined boundaries, wherever possible. This approach ensures stability and reliability. Additional features – offered with our implemented solution include faceted search, dynamic clustering, near real-time indexing and geospatial search.Polar performs index splitting techniques such as “time-based” or “size-based” for log searches.