Narwhal Data Solutions

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Overview

$10,000+

Min Project Size

$50 - $99/hr

Avg Hourly Rate

2 - 9

Employees

2020

Year Founded

ABOUT NARWHAL DATA SOLUTIONS

Unlock the full potential of your data.

Narwhal Data Solutions converts your data into valuable strategic resources by leveraging our specialized skills in data engineering, analytics, and business intelligence. We offer customized and affordable services that lead to tangible business outcomes. At Narwhal Data Solutions, we focus on data-driven insights rather than assumptions. Our goal is to equip businesses with practical information that enhances decision-making accuracy. Allow us to assist you in developing effective data systems!

Tech Stack

HadoopMicrostrategyOracleQlikTechSAPTableauTerradata

Languages Spoken

EnglishGerman

HIGHLIGHTS

$10,000+

Min Project Size

$50 - $99/hr

Avg Hourly Rate

2 - 9

Employees

2020

Year Founded

Services

Focus

Industries

Clients

Services Provided

60% BI & Big Data Consulting & SI

30% Cloud Consulting & SI

10% IT Staff Augmentation

About the Team

OUR STORY

Narwhal Data Solutions was created to provide top-tier, vendor-neutral data engineering, analytics, and data science services. Our mission is to transform complex data into actionable business insights. Led by Dr. Marek Strzelczyk, whose experience spans renowned scientific institutions like CERN, large tech firms, and innovative startups, we bring deep expertise to every project. Since 2017, Dr. Strzelczyk has been consulting independently before assembling a skilled team that now forms Narwhal Data Solutions GmbH, established in Berlin in 2020. We primarily serve small and medium enterprises and startups across Germany and Europe. Committed to factual accuracy and strategic precision, we empower businesses to harness their data effectively, ensuring their decisions are driven by reliable information and competitive advantage.

Portfolio

Integration of macroeconomic data with Camel K

Unknown

Objective: Our goal was to enhance the integration of macroeconomic data for a finance and analytics client. Challenges: The client required a more efficient way to consolidate macroeconomic data from multiple sources and to automate the processes of data collection and storage. Solution: We utilized Apache Camel K, a flexible integration platform, in conjunction with Kubernetes for orchestration. Result: The initiative transformed data integration, improving its efficiency, reliability, and adaptability to varying data sources and formats.

Data platform optimisation

Unknown

A sports tech startup ran a data platform centered around Amazon Redshift, enhanced by Amazon Glue and PySpark, with ETL processes managed by Airflow. The data warehouse was utilized for both internal business intelligence analytics and to supply analytical data for one of the company’s digital products. However, as data volumes increased, performance issues became apparent. The small but ambitious data team did not have enough personnel or specialized technical skills to address these long-term issues. We discovered several weaknesses in the current data infrastructure, resulting in both poor performance and excessive operational costs. After a thorough review of the data and common queries, we implemented a new data partitioning strategy that improved data distribution, minimized skew, and enhanced cluster performance. Additionally, improved ETL processes lessened the load on the cluster, leading to a 2-3x reduction in average query execution times and nearly eliminating excessively long query durations. We then recommended alterations to the data architecture that could boost system performance as data volumes continued to grow, while also achieving significant cost savings by reducing the Redshift cluster size and transferring much of the workload to PostgreSQL, which better fit the observed usage patterns. An MVP was developed to illustrate the operating principles and showcase the solution's performance and scalability potential, after which it was handed over to the internal team for further development.

Scalable data platform

Unknown

A startup in the renewable energy sector gathered and analyzed data from SCADA systems. The data infrastructure and analytics platform were hosted on Azure, featuring a data warehouse built with Data Vault methodology and running on SQL Server as the central component. To meet the need for near real-time data presentation, the data was continuously loaded and processed using streaming and mini-batch techniques. However, performance issues began to emerge despite the relatively low data volume (under 1TB). Anticipating rapid growth, the company aimed to prepare its data platform for a potential scaling increase of 100 times in the near future. They approached Narwhal Data Solutions to assess the current system, existing requirements, and performance challenges, ultimately requesting a new scalable architecture proposal. Various architectural options were suggested, including an enhanced version of the existing system transitioned to SQL Hyperscale, as well as a stream processing system utilizing Apache Kafka with kSQL DB or Flink. Additional technology alternatives were thoroughly examined. Both functional and non-functional requirements were considered, the advantages and disadvantages of the proposed architectures were outlined, total cost of ownership was estimated, and an implementation/migration roadmap was created.

Projects & Clients

Project Size & Rate

Minimum Project Size

$10,000+

Average Hourly Rate

$50 - $99/hr

Pricing Snapshot

Min Project Size

$10,000+

Avg Hourly Rate

$50 - $99/hr

Cost Rating

N/A

What Clients Have Said

"Narwhal Data Solutions delivers quality projects within budget and timeline expectations based on verified client feedback."

Based on verified reviews and internal benchmark data

PROJECT BUDGETS

No budget data yet — reviewers either haven't shared a project budget or kept theirs confidential.

Select a service

BI & Big Data Consulting & SI

Cloud Consulting & SI

IT Staff Augmentation

Locations

Headquarters

Wolliner Str.

Berlin, Berlin, Germany 13355

0000000000

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