In the midst of a revolution driven by data engineering, researchers estimate that every person on earth is generating approximately 1.7 MB of data per second! Today, only 0.5% of accessible data is analyzed and used, as per the MIT Technology Review. This tremendous gap is a boon in disguise for big data technology, services, and advanced data analytics.
Moreover, the Big Data market revenues for software and services are expected to reach $103 billion in 2027. It is no surprise that a survey found that 79% of executives think that companies that do not embrace Big Data analytics, solutions, and strategies may face extinction.
Nitor is proud to capitalize on the tremendous potential that Big Data engineering tools and technology can offer.
Our experts use robust conceptual models to extract insights about four key types of questions:
Our experts work with you during your entire Cloud journey to offer Big Data engineering services and build a customized roadmap so you can reap the benefits of our data and analytics solutions. Following is our proposed architecture, along with technology options, that provides a basis for the deployment of Big Data engineering services:
Retrieve data from disparate data sources (database/SaaS platforms) in order to replicate it to a set destination or data warehouse
Ingest and enrich constant streams of data from heterogeneous sources like web/mobile apps and wearables, for a unified view of your data
Store raw data in a cost-effective data lake. Refine and transfer it to a secure, optimized data warehouse
Refine raw data in data lakes before transferring to data warehouses. Conduct data cleansing procedures with our Data LEGO framework
Organize data in your optimized database. Eliminate data siloes and resolve reporting challenges. Create models for business problems. Decide schema on write/schema on reading strategy across various data sources/sinks
Process data quickly and deliver automated, intelligent insights with sophisticated algorithms using the refined warehouse data
Employ proven ML-based techniques to predict trends and detect potential threats, with the freedom to scale as you grow
Configure unlimited, customized business rules through a data pipeline, and transform the data for your required business rule configuration
Build an API layer to securely access data which acts as a decoupled, language-agnostic data/functionality interface to applications
Write sophisticated algorithms that extract the maximum possible meaning from data and are critical to solving business problems
Address the volume, velocity, value, variety, and veracity of data to derive utility from the insights provided by data scientists
SQOOP, Kafka, Flume, Spark Streaming, Storm, AWS - AWS Glue, AWS Data Pipeline, Azure – Azure Data Factory, Azure EventHub, etc
HDFS, Data Lake store from Microsoft Azure, Casandra, MongoDB, HBase, AWS – S3 storage, Azure – Data Lake Store, Blob storage, etc
Scala, Python, R, PIG, Hive-SQL {HQL}, AWS – Elastic, MapReduce, Azure – ADF Data Flow, Stream Analytics, etc
Python and Spark, R
Qlik, Sisense,PowerBI, Tableau, AWS QuickSight, etc
Nitor Infotech’s Big Data engineering services will allow you to add/remove cloud computing resources to scale infrastructure as well as reduce time to data insights and lower dependency on IT. With this we have:
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