Big Data Services

Unleash the power of your data with Sneek Peak Tech Big Data Services – where innovative solutions meet unparalleled expertise

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Sneak peek Big Data Services

Big data implementation/evolution strategies and detailed roadmaps.
Recommendations on data quality management.
Solution architecture design + an optimal technology stack.
User adoption strategies.
A proof of concept (for complex projects).

Big data solution architecture design.
Solution development (a data lake, DWH, ETL/ELT setup, data analysis (SQL and NoSQL), reporting, and dashboarding).
Setup of big data governance procedures (data quality, security, etc.)
Big data testing and QA.
Software modernization, evolution, redevelopment.

Big data solution infrastructure setup and support.
Solution administration.
- Software updating.
- Adding new users and handling permissions.
Big data management.
- Data cleaning.
- Data backup and recovery.
Solution health checks, performance monitoring, and troubleshooting.

Designing specialized big data analytics solutions for 30+ domains.
Big data visualization.
Real-time big data analytics.
Artificial intelligence.
ML model development and turning.
Natural language processing.
Image analysis.
Data science as a service.
Big data mining.

The Benefits of Sneak peek Big Data Services

With practical experience in 30+ domains, we speak your language, understand your unique challenges, and offer pragmatic solutions that fit your processes.

We use our DevOps and Agile expertise to build efficient development processes, apply feasible test automation, and rightsize cloud resources to reduce cloud fees.

We set up automated data governance and reporting procedures to eliminate manual work for your IT and BI teams and reduce the risk of human errors.

Enjoy the complete clarity of your big data dashboards: we build easy-to-read reports and responsive interfaces that easily adapt to users’ needs (e.g., sleek visuals for C-level presentations, in-depth data exploration for analysts).

We establish robust big data quality management processes that ensure your data is always accurate, consistent, and complete to serve as a trustworthy source for analytics.

We combine best-fit algorithms and create tailored data sets for model training, apply cross-validation to fine-tune hyperparameters and enable self-learning for ML engines to deliver consistently accurate AI output.

How It Works: Big Data Components We Cover

A bus layer or aggregation layer collects data from various sources, handles event sequencing, timestamping, and routing.
A data lake stores collected raw data.
A batch processing layer extracts data from the data storage in a scheduled manner (entails the latency from minutes to hours) and transforms it into analyzable formats to be further processed by the analytics layer.
A stream processing layer captures real-time data and handles real-time in-memory processing (entails latency from milliseconds to seconds).
A serving component (a data warehouse) stores processed data.
A big data governance layer handles data auditing, security, quality, cataloging, metadata management, etc.

Big Data Implementation Steps

Real-life big data implementation steps may vary greatly depending on the business goals a solution is to meet, data processing specifics (e.g., real-time, batch processing, both), etc. However, from Sneak peek tech’s experience, there are six universal steps that are likely to be present in most projects. Go through them in the guide below or download the roadmap in .pdf to consult later.