Data Engineering. Advanced Analytics.
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.
Data Engineering Services
Data lake and data warehouse
Axacraft assists you in storing structured, semi-structured, unstructured, and binary data aggregated from disparate sources. With current and historical data gathered in a single point, you get a consolidated view of your business processes.
Data migration
Axacraft ensures smooth migration of massive data arrays to cloud storages without data loss or downtime. We launch new suitable infrastructures, as well as prepare and optimize data for migration, while taking care of security.
Continuous integration and deployment
Hire a team of certified experts to enable smooth integration of custom apps with third-party systems and aggregate data from multiple sources for further analysis. At Axacraft, we integrate disparate data sources by implementing the ETL pipelines. Our engineers and consultants help you to maintain consistency of data landscape, as well as guarantee an error-free data flow.
Data analytics and visualization
Develop data management solutions to visualize and analyze data for informed decision-making. Increase the visibility of business processes, automate reporting, and get valuable data insights in real time.
Data storage and ETL processing
We perform data extraction, cleansing, profiling, normalization, and transformation. Our team also enables data processing, mining, and warehousing. We will help you to design ETL pipelines based on Hadoop HDFS, Amazon S3, GCP Cloud Storage, Azure Blob Sore, and the DSE distributed file system.
Data pipeline development and implementation
Our experienced data engineers build and implement scalable data pipelines for businesses across various industries. We make sure your data flows are designed properly, so that you can stay focused on business-critical tasks.
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.
Data engineering can provide the infrastructure to support advanced data analytics and machine learning, driving innovation and new business opportunities.
Better data management and analysis can lead to more informed decision making, resulting in improved business outcomes.
Data Technologies
Cloud toolset
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
Open Source
Support for all Hadoop distributions: Cloudera, Hortonworks, MapR
Hadoop tools: hdfs, hive, pig, spark, flink
No SQL Databases: Cassandra MongoDB, Hbase, Phoenix
D3.js
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.
We can train and upskill your data team members.
Axacraft supplements client teams with data scientists, data engineers, and data analysts. Other advanced positions, such as data management and data governance are often part of our client projects.
Gain customer insights to reveal details about habits, demographics, preferences, and aspirations.
Axacraft can supplement experience teams or provide a foundational understanding of data science which can help make sense data and how to leverage it to improve user experiences and inform retargeting efforts.
Increase security:
Use data science to increase your business’s security and protect sensitive information. For example, machine-learning algorithms can detect bank fraud faster and with greater accuracy than humans, simply because of the sheer volume of data generated every day.Inform internal finances:
Your organization’s financial team can utilize data science to create reports, generate forecasts, and analyze financial trends. Data on a company’s cash flows, assets, and debts is constantly gathered, which financial analysts use to manually or algorithmically detect trends in financial growth or decline.Streamline manufacturing:
Manufacturing machines gather data from production processes at high volumes. In cases where the volume of data collected is too high for a human to manually analyze it, an algorithm can be written to clean, sort, and interpret it quickly and accurately to gather insights that drive cost-saving improvements.Predict future market trends:
Collecting and analyzing data on a larger scale can enable you to identify emerging trends in your market. By staying up to date on the behaviors of your target market, you can make business decisions that allow you to get ahead of the curve.