Data Engineering and Data Science Services

Gather, organize, analyze and operationalize large data sets to deliver a personalized customer experience and increase your competitive advantage.

Data engineering services that provide real-time business insight

The rise of AI and a daily influx of complex data sets means companies need to find and take advantage of the most useful ones. Data engineering consultants enable companies to leverage AI solutions to gain a better business understanding and react in real time. With the Software Mind team, you can quickly discover, access, analyze, share and operationalize data to create actionable insights that drive your business growth.

Data engineering strategies that deliver results

Prepare your data for AI

The success or failure of implementing AI solutions lies in your data. Data is also a factor that will differentiate you from your competition. Prepare your data streams to be safely used to train and execute your large language models (LLMs).

Reduce costs and prepare your data streams for increased amounts of data

Handle the constantly increasing amount of data your company processes by implementing data streams that are cost-efficient and robust enough to prepare your company for the future influx of additional information.

Gather insightful data from multiple internal and external sources

Leverage device-based and other data sources to implement a scaled solution that combines predictive and prescriptive models. Integrate data using off-the-shelf processing solutions customized to your company's needs.

Forecast business and development risks

Predict future market trends by collecting, processing and analyzing data sets – while assessing project risks and taking advantage of suggested alternative solutions. Data engineering helps companies improve their decision-making processes and enables them to grow faster than their competition.

Increase your company’s operational intelligence and effectiveness

Optimize production performance and detect deviations or undesirable patterns. Apply data-driven algorithms to eliminate failures and gather insights to prevent costly or unexpected challenges by finding and removing their possible sources.

Protect privacy and reduce cloud costs in a swarm of mobile devices

Support data-driven decisions in a distributed cloud of mobile devices and develop innovative products that process sensitive data and run models in a resource-limited environment. Federate data, generate insights and drive decisions without compromising user privacy. Maximize cloud value by leveraging local processing capabilities on devices.

Provide behavior analysis and marketing automation solutions

Gain customer insights by applying data science to identify and analyze customer behavior patterns. Improve user experience by introducing more personalized solutions and mitigate potentially negative customer-related decisions.

Data engineering services to empower your offer

Building or modernizing AI-ready data stream

Modernize data streams you have or build your company’s first AI-based one with engineers who have a proven record of success.

Preparing existing data streams for AI usage

Maximize the data you have by getting it ready to be optimized by AI solutions.

Providing data engineering teams

Partner with autonomous teams who can take ownership of your data processes, strategies and solutions.

Staff augmentation of data specialists

Enhance your project with proven, cost-effective data scientists, data analysts, data engineers, and cloud experts.

Moving on-premises data streams to the cloud

Migrate your data and data pipelines swiftly and securely to cloud solutions.

Implementing business intelligence (BI) and smart data insight solutions

Integrate the latest innovations to enhance operational efficiency and drive decision-making.

Maintaining existing data streams

Transfer the responsibilities of monitoring, maintaining and evolving your data streams to a reliable team – so you can focus on your core business.

Data engineering consultants who companies trust

01

19 years’ experience with AI and Big Data solutions

Our data engineering experience dates to 2005, when we developed technology for the first web-scale Semantic Web startup. This innovative project consisted of building a clustered solution for scalable Natural Language Processing (NLP), Europe’s first commercial use of Hadoop and one of the first implementations on the global stage. We provide Big Data architecture and analytics and have delivered data science and AI solutions to companies working with structured, semi-structured and unstructured data at any scale.

19 years’ experience with AI and Big Data solutions

01

19 years’ experience with AI and Big Data solutions

Our data engineering experience dates to 2005, when we developed technology for the first web-scale Semantic Web startup. This innovative project consisted of building a clustered solution for scalable Natural Language Processing (NLP), Europe’s first commercial use of Hadoop and one of the first implementations on the global stage. We provide Big Data architecture and analytics and have delivered data science and AI solutions to companies working with structured, semi-structured and unstructured data at any scale.

Who we’ve helped

What our clients say

What really stuck out about Software Mind was the quality of the talent, along with an engineering culture that reflects a Silicon Valley mindset; fast iteration, innovation and development that considers the right trade-offs. The attitudes and approaches at Software Mind, from the leadership to every engineer on the team, matched our own. The culture of Software Mind was like ours, so we knew integrating their team with ours would be smooth.

Dmitri Gaskin, Co-Founder of Branch

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Our data engineering approach

Software Mind has expertise and knowledge of implementing all phases of data processing, from gathering data to generating reports and business insights.

The process is as follows: first Extraction, then Operations, then Storage, then Analytics. Operations and Analytics are about AI. The entire process concerns Data Governance and Cloud.The process is as follows: first Extraction, then Operations, then Storage, then Analytics. Operations and Analytics are about AI. The entire process concerns Data Governance and Cloud.

1. Data extraction

We can extract large amounts of data from any data source – from your company’s internal database and files, through external websites and systems (like Salesforce) to hardware devices (cell phones, IoT devices).

Activities

  • Extracting data from databases (SQL and NoSQL)
  • Extracting data from flat files (like CSV) and spreadsheets
  • Taking data from external systems/APIs (like Salesforce, LinkedIn, Facebook)
  • Crawling websites / Web scraping
  • Collecting data from IoT/hardware devices
  • Gathering data from mobile devices

Technologies

  • Bots / high-performance Python scripts
  • VPN solutions for web scraping
  • ETL solutions (Google Cloud Dataflow, Azure Data Factory, Integrate.io)
  • Embedded devices and IoT (custom APIs, Azure IoT Hub, AWS IoT Core)
  • Queries to Data Lakes and Data Warehouses

2. Data operations

We can create scheduled or on-demand data flows in almost real-time to be stored in data lakes or easily consumed by other services (like BI tools or AI solutions).

Activities

  • Cleaning and filtering data
  • Curating and preparing data for training LLMs/AI
  • Aggregating data (data streams and batch processing)
  • Transforming data (data streams and batch processing)
  • Checking data quality
  • Verifying data security

Technologies

  • Big Data processing: Databricks, Spark, Hadoop
  • Data streaming: Apache Flink, Low-latency custom Java services, Pandas
  • Message queues: Kafka, Amazon SQS, Google Pub/Sub
  • Function programming: AWS Lambda, Azure Functions, Google Cloud Functions
  • Other: Apache Airflow, EKS, ECS, AKS, Kubernetes

3. Data storage

We can design and implement intermediate data stores or warehouses and data lakes that are tailored to the needs of your organization. This includes data segregation by security levels and low-cost data archives.

Data storing capabilities

  • Multi-level Data Lakes
  • SQL databases
  • NoSQL databases
  • Data prepared for machine learning
  • Moving and storing data in low-cost data archives

Technologies

  • Data lakes: Snowflake, AWS Data Lake, Google Data Lake, Azure Data Lake, Cloudera
  • Low-cost data archive: S3 Glacier, Azure Archive Storage, Google Archive Storage
  • NoSQL databases: mongoDB, elastic search, redis, foundation DB, CosmosDB, Azure Table Storage, Google Cloud Firestore, Google Clould Bigtable, Amazon DocumentDB, Amazon DynamoDB
  • Special purpose databases: FoundationDB, Oracle Spatial Database, Neo4j
  • Time-series databases: InfluxDB, Prometheus, IoTDB

4. Data analytics

We provide meaningful insights for your organization through AI, data science, machine learning and BI solutions that analyze aggregated data.

Activities

  • Designing and implementing machine learning models
  • Providing data science specialists
  • Designing and implementing AI solutions
  • Implementing BI solutions

Technologies

  • BI solutions: Power BI, Tableau, Qlik, Looker
  • Jupyter notebook
  • AI solutions: Llama, ChatGPT, BERT, Falcon, Hugging Face, TensorFlow, Keras

Data engineering projects we’ve delivered

Check out over 15 examples of our data engineering projects from different industries, including a breakdown of tech stacks and the size of processed data.

Data science insights

Get expert advice and best practices on developing data science solutions, designing database architectures and enhancing analytics.

Turn data into actions that generate value

1500

+ experts

25

+ years of innovation

250

+ clients who trust us

Data engineering services – FAQ

How do data engineering services differ from data science services?

Data engineering services are responsible for the construction, optimization and management of data pipelines and their infrastructure. In contrast, data science services are focused on utilizing the outputs of these data pipelines to access data and derive insights using statistics or advanced machine learning algorithms. In short, data engineering involves the development and maintenance of the infrastructure that handles data, while data science is the process of extracting insights and knowledge from that data.

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