Data labs by Scalac

We help you get the most out of your data

Flink
Flink
Spark
Spark
Google Cloude
Google Cloude
Druid Apache
Druid Apache
hadoop
hadoop
AWS
AWS
Kaffka
Kaffka

Unreliable data pipelines result in a waste of energy, work, and computing power.
This means more time is needed to ship the features. These days, businesses need reliable data products. We employ a number of technologies and techniques on top of Apache Spark to save money on computing power, and to minimize the time your employees spend on fixing bugs.

Data delivery

The costs and time to implement solutions on entire organization are sometimes deadly for businesses and cause stalling of daily operations. Our competences let us get exactly the data you need – we carefully analyze and prepare a system that will generate useful insights in a fraction of the cost and time of a full-blown Business Intelligence Solution.

Data engineering

Our Analysts get to know and learn to understand your business. Our Data Engineers prepare and structure your data. UI Designers and Front-End Engineers put it all together to create a customised visual representation of your key performance indicators. YOU find and share the answers to your most important questions.

Machine learning engineering

Our Analysts get to know and learn to understand your business. Our Data Engineers prepare and structure your data. UI Designers and Front-End Engineers put it all together to create a customised visual representation of your key performance indicators. YOU find and share the answers to your most important questions.

Our Process

We learn how your company works with the data. We offer a data audit process that will last just a few days and will be followed by a summary containing suggestions and recommendations regarding data processing optimisation and system architecture.

We kick off the project once the audit is completed, and the project scope is decided. We work in an agile environment. We provide demos, training, and extensive documentation.

When the project approaches the finishing line we provide a hand-over session with extensive explanations and insights into the delivered solution.

We support your internal teams after the project has finished. Our goal is to use our expertise to help you succeed and use the full potential of your data and your team.

Why Scalac

Data reliability and robustness is the key

Massive data revolution brings a lot of opportunities, but it also poses new challenges. We have learned how to acquire, process and consume data, however, we still struggle with data reliability. Traditional software engineers know how to test software, but in the world of data engineering, such an approach simply does not scale. Scalac data engineering team addresses exactly those issues by building highly reliable and robust data pipelines on top of Apache Spark.

End-to-end solutions that reduce the threat of expensive data backfills

Scalac offers end-to-end solutions, reliable data pipelines that provide the safety of your data in terms of data structure and reliability. Proper data handling can save hours of Machine Learning teams a lot of work, ensure customers consuming your APIs always receive what they expect, make your infrastructure more performant by reducing the threat of expensive data backfills due to a minor mistake.

Corrupted data safety system

Data related bugs are hard to spot. Corrupted data can have a significant impact on your daily operations – it can yield improper insight which leads to suboptimal decisions, impair the work of your data teams and malform your data products. Once spotted, often weeks after they first appear, they require a lot of attention from your data engineering teams and consume a significant amount of computing resources. Scalac experts remove or mitigate those issues with reliable data pipelines and Apache Spark.

Our Projects

For one of our clients, we prepared a plan on how to perform Sentiment Analysis based on his business context. We recommended using the Aspect based Sentiment Analysis (called ABSA), where the sentiment polarity associated with the aspect-target is determined. Our approach uses end-to-end the neural network, and as a base is a pre-trained language model. We use the language model BERT. The model understands the general language dependencies.

Machine Learning models are great and powerful. However, the usual characteristics of regular training can lead to serious consequences in terms of security and safety. We prepared an analysis of this issue to show a way to create more robust and stable models that use features that are more meaningful to humans. In our experiments, we used a simple binary classification to recognize the digits zero and one from the MNIST dataset.

As an Adtech organization, Tapad had been using an Apache Spark cluster as an important element of the ETL process to distribute large data sets to be processed and distributed to 3rd parties. Our primary goal was to develop integrations using the above-mentioned Apache Spark.

Let’s talk about your project

    We will reach out to you in less than 48 hours
    to talk about your needs.

    We will perform a free tech consultation
    to see which stack fits your project best.

    We will prepare the project estimate in 3 days
    including the scope, timelines, and costs.

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