CodeSizzler approach for Big Data
- Hadoop services
- Data engineering
- Advanced analytics
Manage your data
We offer strategic options for organizations to engage with our team and quickly leverage expert resources to rapidly accelerate the analytical value of your data.
Data management
Store and process vast quantities of data in a storage layer that scales linearly.
Data Governance & Integration
Quickly and easily load data, and manage according to policy.
Data access
Interact with your data in a wide variety of ways – from batch to real-time.
Security
Address requirements of authentication, authorization, accounting and data protection.
Optimize your data
Design, build the most optimal data flow: from integrating data sources and implement big data applications on Apache Hadoop with the latest open source tools.
Application development
We help you design, build and optimize your big data applications on Apache Hadoop with the latest open source tools.
Solution design
Designing and implementing comprehensive solutions including – hardware infrastructure, data sources, ecosystem software and operation considerations for optimum performance within your environment.
ETL
ETL plan includes identifying multiple data sources and file formats, transforming and loading them into data structures best suited for your needs.
Data aggregation
Data aggregation to quickly and accurately consolidate and fuse data from various sources.
Insights for smarter decisions
Assess your business needs, define the business case, and recommend the suitable machine learning methods. Get new insights to make confident decisions in minutes.
Use case discovery
Understand business priorities and data sources available for analytics and identify & roadmap for big data development and training.
Statistical method
Develop right algorithm and statistical model to meet your speed, reliability and maintenance goals.
Data modelling
Transform and structure raw data to create a custom data model for your business needs.
Machine learning
Assess your business needs, define the business case, and recommend the appropriate machine learning methods.
Data Lake
The data lake is essential for any organization who wants to take full advantage of its data. The data lake arose because new types of data needed to be captured and exploited by the enterprise. As this data became increasingly available, we use these data to discover insight through new applications to serve the business. We can then use a variety of storage and processing tools, typically tools in the extended Hadoop ecosystem, to extract value quickly and inform key organizational decisions.
Analyse and store petabyte of data:
Data Lake was architected from the ground up for cloud scale and performance. With Azure Data Lake Store your organization can analyze all its data in a single place with no artificial constraints. Your Data Lake Store can store trillions of files where a single file can be greater than a petabyte in size which is 200x larger than other cloud stores.
CodeSizzler simplifies real-time data integration to Azure Storage solutions – including Data Lake Storage (Gen 1 and Gen 2) and Blob Storage – from a wide variety of sources.
You can continuously deliver data from enterprise databases via log-based change data capture (CDC), cloud environments, log files, messaging systems, sensors, and Hadoop solutions.
The CodeSizzler solution enables you to quickly build streaming data pipelines with your desired data potential (real-time, micro-batch, or batch) and enrich the data with added context. These pipelines can then support any application or advanced analytics / machine learning solutions – including Azure SQL Data Warehouse and Azure
Databricks – that use Azure Storage services. With access to prompt data in the right format, your data operations teams can significantly reduce the preparation effort for analytics, and your organization can achieve faster time-to-insight.
Use case | Retail Analytics
“Your smart choice of choosing smart people who focus on your data to be accessed, analyzed, and put them to work for the business growth.”
Our customer – collects 2.5 petabytes of unstructured data from 1 million customers every Day. Part of that vision is to modernize its datacenter infrastructure, migrating on-premises workloads to the cloud.
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Models Process
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Orders/Hour
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New data points/Month
Technology expertise
