Automate your data pipeline with data engineering services. Get infrastructure for automatically processing your data into data warehouses or data lakes.
years of experience in data engineering
clients worldwide
in-house employees
We design and implement pipelines that move data from multiple sources to your centralized repository. Design ETL pipelines to save on cloud computing or go with ELT for faster processing. Ensure seamless data flow, reduce manual effort, and improve data accuracy.
Our data engineers build scalable data warehouses and data lakes. Data warehouses carry our ETL pipelines, and data lakes are perfect for ELT. In any case, we create customized repositories optimized for your business goals.
Don’t know where to begin with data engineering? Or have a roadblock in the way? Our senior data engineers will help you out. Develop the implementation strategy and overcome any obstacle.
It’s not enough to collect a ton of data – the data must be good, too. That’s why we make your data reliable and clean. We’ll get rid of errors, duplicates, and inconsistencies in it, so you can start on a strong foundation.
Migrate, manage, and optimize your cloud infrastructure. We work with all the major cloud platforms like AWS, Azure, and Google Cloud. Enable cost-effective and flexible data management.
Want to get live insights from your data? We set up data streaming, so you can handle high-volume, high-velocity data streams. Enable real-time analytics and decision-making with Pub/Subs instead of APIs.
The client is a mid-sized US logistics company that provides order fulfillment as well as warehousing and storage, picking and packing, and shipping services.
Discover how a US-based eLearning provider transformed K-12 education with an AI-powered highlights generator, reducing content update costs by 70%.
The customer is a US eLearning provider for K-12 focused on professional development solutions, STEM curriculum & programs.
The most amazing thing about our partnership is that in many situations we don’t have the time to map out product specifications and requirements and yet the final product almost always exceeds our expectations. This has been very inspirational for our team and continues to make our partnership even more meaningful.
Aristek Systems is an integrated resource within our organization structure to provide all involved in the process with optimum use of their time. Our collaboration resulted in approximately a 45% market share the first year within the only state the product was released. Today we are in all 50 states offering customized solutions, customer service and marketing services.
I had the privilege of working with Ruslan and his team at Aristek for the past three years. It is hard to find that level of dedication in an offshore vendor/partner but Ruslan and the Aristek team deliver it. I would highly recommend Ruslan and Aristek to anyone needing an expert level partner in EdTech and SaaS platform development.
We will help you design the pipeline that serves your business interests best.
Not sure if you need data engineers or data scientists? Getting into data is hard, but we’ll keep it simple. We will help you navigate around the data world.
You are in good hands, because we cover all sorts of data-related service. Whether you need help with analyzing data, building a model, or if you just want to move to the cloud – we are here to help.
Discover the backbone of our data engineering prowess with our technology stack. From leading cloud service to powerful processing frameworks and specialized software, explore how we harness innovation to drive data-driven success.
Hadoop is a distributed storage and processing framework designed to handle big data. It provides a distributed file system (HDFS) and a framework for the processing of large datasets across clusters of computers using simple programming models.
Apache Spark is an open-source distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. It’s commonly used for big data processing and analytics.
Apache Kafka is a distributed event streaming platform used for building real-time data pipelines and streaming applications. It’s designed to handle high throughput and offers features such as durability, scalability, and fault tolerance.
Databricks is a unified analytics platform that provides a collaborative environment for data science and engineering teams to work together. It’s built on top of Apache Spark and offers managed Spark clusters, interactive notebooks, and other tools for data analysis and machine learning.
Snowflake is a cloud-based data warehousing platform that allows users to store and analyze data using SQL queries. It’s known for its scalability, performance, and ease of use, particularly in multi-cloud environments.
Airbyte is an open-source data integration platform that helps organizations replicate data from various sources to data warehouses or lakes. It offers connectors for popular data sources and destinations, along with features for orchestration and monitoring.
DBT is an open-source tool that enables data analysts and engineers to transform data in their warehouses using SQL. It’s commonly used for building data pipelines and managing transformations as part of the data analytics workflow.
Our engineering service streamlines data processing and analysis. Start getting data insights for your business.
Data engineering is all about processing data. Companies have multiple data sources: CRM, ERM, Google Analytics, etc. Data engineers automate the data flow from all the sources to a single repository. Then, they cleanse the data so that other department can analyze and gain insights.
In a way, data engineering is like plumbing. Engineers pipe data from multiple sources to a single repository – and they make sure the data is clean enough to use.
Data science focuses on analyzing and extracting insights from data. Data scientists uncover patterns and trends in data. They use techniques like machine learning and statistical analysis for that.
Data engineering is all about building and managing the infrastructure that allows data scientists to do their magic. They focus mainly on collection, storage, and processing of large volumes of data.
So, while data engineers lay the groundwork, data scientists dive into the data to uncover its secrets.
You need data engineering when you want to make sense of your data. If you’re dealing with large volumes of information, multiple data sources, or complex data transformations, data engineering can help. Whether you’re looking to build data pipelines, optimize data storage, or streamline data processes, data engineering is your go-to solution for turning raw data into valuable insights. So, if you’re serious about harnessing the power of your data, it’s time to consider data engineering.
Absolutely! We’re here to help you navigate the world of data engineering with expert guidance and support. Our data engineering consulting services are designed to assist you at every stage of your data journey. Whether you’re starting from scratch or looking to optimize your existing data infrastructure, our team of experienced professionals is ready to provide tailored solutions to meet your specific needs. From strategy development to implementation and beyond, count on us to be your trusted partner in unlocking the full potential of your data.
A data pipeline is like a highway for your data—it’s a series of steps that allows you to move data from one place to another in a smooth and efficient manner. Just like how water flows through pipes in your house, data flows through a data pipeline.
It starts with data collection, where raw data is gathered from various sources. Then, it goes through processes like cleaning, transforming, and enriching to make sure it’s ready for analysis. Finally, the data is loaded into a destination, such as a database or a data warehouse, where it can be accessed and analyzed by users. Essentially, a data pipeline helps you organize and streamline the flow of data in your organization, making it easier to work with and derive insights from.
Data governance is like the rulebook for your data – it’s a set of policies, processes, and controls that ensure the quality, integrity, and security of your data throughout its lifecycle. In data engineering, data governance involves defining standards and guidelines for how data is collected, stored, processed, and accessed within an organization.
This includes:
Data engineering is crucial because it builds the infrastructure to efficiently process, integrate, and maintain high-quality data. This enables organizations to make informed decisions, scale their operations, and drive innovation using data-driven insights.
Data engineering works by collecting, storing, processing, and integrating data efficiently and securely. This involves designing data pipelines, managing data quality, and ensuring compliance with regulations to enable informed decision-making and drive business success.
It depends on your specific requirements and preferences. Here’s a simplified breakdown:
Ultimately, the decision between ETL and ELT depends on factors such as your data volume, transformation complexity, target system capabilities, and performance requirements. We can help you evaluate your needs and determine the best approach for your data pipeline.
CDC stands for Change Data Capture. It’s a technique used in data engineering to identify and capture only the changes made to a database since the last update, rather than capturing the entire dataset each time. CDC allows data engineers to efficiently track and replicate changes in real-time, making it particularly useful for applications like data integration, replication, and synchronization. By capturing only the changes, CDC minimizes processing overhead and ensures that downstream systems have access to the most up-to-date data.
