Data Engineering & Advanced Analytics.
Data forms the foundation.
Axacraft offers full data engineering teams and data engineering managers as a cohesive squad. Finding an entire, cohesive data engineering team that works well together is rare. That's why Axacraft offers data engineering teams in one place. Our team of qualified and experienced Data Engineers and Data Scientists will create high-performance infrastructure and optimize your data to help you make better decisions and achieve your business goals.
Our data services start with discovery.
This process helps ensure our client's needs are understood and met.
Data Engineering Services
E-commerce & Retail
Customer Lifetime Value
We help you double down on the customer segments and journeys that are working, and fix the ones that aren’t.
- Optimize website conversion rate
- Identify high performing customer cohorts
- Create customized product recommendations
Marketing Analytics
We create robust, automated reporting so your channel managers know what is working and can focus on what they do best.
- Centralize marketing performance data
- Automate digital and offline marketing reporting
- Build custom attribution models for your business
Personalization
We enable personalized customer communication across email marketing, paid advertising, and customer support channels.
- Create a 360º customer profile
- Integrate with your martech stack
- Power personalized multi-channel customer journeys
A data science team in a box? We don't like to call ourselves that but...
What benefits can corporations realize with more data engineering support?
By implementing data pipelines, data can be transformed, cleansed, and integrated, resulting in improved data quality.
By integrating data from multiple sources, data engineering can make data analysis more comprehensive, giving better insights into the business.
Data engineering can automate data-related processes, reducing manual efforts and increasing efficiency.
Better data management and analysis can lead to more informed decision making, resulting in improved business outcomes.
The data technologies we utilize
Our Axacraft team of data science experts leverages a variety of cutting-edge technologies to help clients derive insights and make data-driven decisions. From data cleaning and exploration to predictive analytics and machine learning, we use a range of tools such as Python, R, SQL, Spark, TensorFlow, and Keras to analyze and model data. We also work with Big Data technologies such as Hadoop, Hive, and Pig to process and store large volumes of data. Our team has extensive experience working with cloud-based data platforms such as AWS, Google Cloud, and Microsoft Azure, allowing us to build scalable and robust data solutions for our clients.
Analytical Databases: Big Query, Redshift, Synapse
ETL: Databricks, DataFlow, DataPrep
Scalable Compute Engines: GKE, AKS, EC2, DataProc
Process Orchestration: AirFlow / Composer, Bat
Platform Deployment & Scaling: terraform, custom tools
Power BI, Tableau, Data Studio
Tableau
Data Studio
D3.js
Support for all Hadoop distributions: Cloudera, Hortonworks, MapR
Hadoop tools: hdfs, hive, pig, spark, flink
No SQL Databases: Cassandra MongoDB, Hbase, Phoenix
D3.js
Analytical Databases: Big Query, Redshift, Synapse
ETL: Databricks, DataFlow, DataPrep
Scalable Compute Engines: GKE, AKS, EC2, DataProc
Process Orchestration: AirFlow / Composer, Bat
Platform Deployment & Scaling: terraform, custom tools
Python: numpy, pandas, matplotlib, scikit-learn, scipy, spark, pyspark & more
Scala, Java
SQL, T-SQL, H-SQL, PL/SQL
Common data tasks we're asked to conduct
Data discovery is identifying and exploring data assets, while data maturity assessment evaluates how well data is managed and utilized to achieve business goals.
Data quality checks assess data accuracy and completeness, while standardization services ensure data is consistent and conforms to established norms and guidelines.
Cloud-based scalable solutions enable processing and storage of large data volumes, with the flexibility to increase or decrease resources based on demand.
Real-time data processing is analyzing data immediately upon arrival, while batch data processing analyzes data in groups at set intervals.
Optimization of database and data warehouse platforms involves improving performance and efficiency, minimizing resource usage, and enhancing data accessibility and security.
Advanced data analytics is the process of analyzing complex and diverse data sets using advanced statistical and machine learning techniques to derive insights and predictions.
Web API and data streaming development is creating software interfaces that allow data to be accessed and exchanged between different applications and platforms in real-time.
Data migration is the process of moving data from one system or format to another, while ensuring its integrity, security, and compatibility with the new environment.