Today’s organizations generate a huge amount of data that needs to be optimized and transformed into useful business knowledge. Our team of qualified and experienced Data Engineers and Consultants will create high-performance infrastructure and optimize your data to help you make better decisions and achieve your business goals.
Data Engineering Services We perform:
- Data discovery & Data maturity assessment
- Data quality checks and standardization services
- Cloud based scalable solutions for large volumes of data
- Real-time and batch data processing
- Optimization of database and data warehouse platforms
- Advanced data analytics
- Web API & data streaming development
- Agile and customer-oriented work process
Data Architecture
- Design DE cloud solutions designed by a team of certified experts
- Preparation and automation of ETL processes for multi-structured data
- Design optimization of database and data warehouse platforms
- Preparation of software solutions for integrating systems
Data Processing
- Implementation of scalable data driven solutions
- Real-time and batch data pipelines & processing
- Data quality and standardization services
- Data integration & maintenance services
- Data Lakes and Data Warehouses setup
Data Analytics
- Consultancy and preparation of a plan for analytical method development
- Preparation of recommendation systems
- Searching for patterns in sequential data
- Quality evaluation of organizations’ analytical products
Benefits of Data Engineering Service
Become a data-driven organization with the meaningful insights required to make business decisions immediately.
How can your organization benefit from Data Engineering services?
- Making better decisions based on the powerful knowledge you gain from the data
- Improving product quality and user experience by analyzing gathered data
- Identifying new business opportunities by predicting behaviors based on historical data
- Reducing costs and increasing profits by simplifying your data architecture
- Reducing of project duration by speeding up the processes of accessing insights
Our Clients
Here are some of our clients that trust us with their online solutions
Our Technology Tool Stack
Cloud Toolset
- Analytical Databases: Big Query, Redshift, Synapse
- ETL: Databricks, DataFlow, DataPrep, Qlik Replicate, Fivetran, Matillion, Streamsets and Hevo
- Scalable Compute Engines: GKE, AKS, EC2, DataProc
- Process Orchestration: AirFlow / Composer, Bat
- Platform Deployment & Scaling: terraform, custom tools
Open Source
- Support for all Hadoop distributions: Cloudera, Hortonworks, MapR
- Hadoop tools: hdfs, hive, pig, spark, flink
- No SQL Databases: Cassandra MongoDB, Hbase, Phoenix
- Process Automation: oozie, Airflow
Visualisation Tools
- Power BI
- Tableau
- Data Studio
- D3.js
- Qlikview
- QlikSense
- Cognos
Programming Skills
- Python: numpy, pandas, matplotlib, scikit-learn, scipy, spark, pyspark & more
- Scala, Java
- SQL, T-SQL, H-SQL, PL/SQL
Data platforms
- Snowflake
- Azure Synapse
- Databricks
- Bigquery
- Salesforce
- Sap
- Teradata
Data Engineering FAQ
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1. What is data engineering?
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2. What skills are required for a data engineer?
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3. What tools and technologies are commonly used in data engineering?
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4. What are some common data engineering challenges?
Data engineering is the process of designing, building, and maintaining the infrastructure required to store, process, and analyze large volumes of data. Data engineers are responsible for developing data pipelines, data warehouses, data lakes, and other data-related infrastructure to support data-driven decision-making.
Data engineers should have a strong background in computer science, mathematics, and statistics. They should also have experience with programming languages such as Python, Java, and SQL, as well as with data warehousing, data lakes, and data integration tools. Strong communication and collaboration skills are also important, as data engineers often work closely with data scientists, business analysts, and other stakeholders.
Common tools and technologies used in data engineering include Apache Spark, Apache Kafka, Apache Hadoop, Apache Hive, Apache Airflow, Apache Beam, and Apache NiFi. These tools and technologies are used to build data pipelines, data warehouses, data lakes, and other data-related infrastructure.
Some common data engineering challenges include data quality issues, data integration challenges, data security and privacy concerns, and data governance challenges. Data engineers must work to address these challenges and ensure that data is accurate, consistent, secure, and accessible to the right people at the right time.
