Data Engineering
Data collection, creation of pipelines and data architects, ETL and their literacy – all this is assigned to Data Engineering. Amazinum specialists create a data product qualitatively and professionally
What is Data Engineering?
Zippia research predicts that the data engineering job market will grow by 21% between 2018 and 2028.
Fior Markets reports that the global market for big data and data processing services is expected to grow from USD 32.45 billion in 2017 to USD 123.89 billion by 2025 at a CAGR of 18.2% during the forecast period 2018- 2025 years.
How does it Work?
The data engineering process is a sequence of tasks that turn a large amount of raw data into a practical product meeting the needs of analysts, data scientists, machine learning engineers, and others. The data processing process includes the following points:
Moves data from multiple sources to a target system.
Adjusts disparate data to the needs of end users (includes removing errors and duplicates, normalizing data, and converting it into the needed format).
Delivers transformed data to end users.
Data Lake
When the data is raw, they are located in a data lake. A data lake is a centralized repository that allows storing of all structured and unstructured data at any scale. It stores relational data from line of business applications, and non-relational data from mobile apps, IoT devices, and social media. The structure of the data or schema is not defined when data is captured. Data Lakes are an ideal workload for deploying in the cloud because the cloud provides performance, scalability, reliability, availability, and massive economies of scale.
Pipeline
The data pipeline can automate the engineering process. A data pipeline receives data, standardizes and moves it for storage and further handling. Constructing and maintaining data pipelines is the core responsibility of data engineers. Among other things, they write scripts to automate repetitive tasks. Pipelines are used for:
- Sales forecasting;
- Risk assessment;
- Customer segmentation;
- Predictive analytics in customer success teams.
ETL pipeline
ETL pipeline is the most common architecture that has been here for decades. It automates the following processes:
When the data is transformed and loaded into a centralized repository, they are ready for further analysis and business intelligence operations, generating reports, creating visualizations, etc.
Data warehouse
A data warehouse is a central repository storing data in queryable forms. Without this, data scientists would have to pull data straight from the production database and may report different results to the same question or cause delays and even outages. Serving as an enterprise’s single source of truth, the data warehouse simplifies the organization’s reporting and analysis, decision-making, and metrics forecasting.
We are Experienced in:
Data Engineering Tools
Apache Hadoop
Apache Spark
Python
Relational and
Non-relational Databases
Julia
Relational and non-relational databases
Structured data can easily be stored in a relational database.
Semi- or unstructured data uses non-relational or NoSQL databases
SQL and NoSQL databases:
- MySQL
- Microsoft SQL Server
- PostgreSQL
- MariaDB
- MongoDB
- Firebase
- Elasticsearch
- Apache Cassandra
- Apache HBase
Databases in memory
When processing big data, it is necessary to introduce certain caching systems, which is exactly what databases are needed for. They eliminate disk I/O.
In-memory data stores
- Redis
- Memcached
- SAP HANA
- SingleStore
- Oracle TimesTen
Corporate data warehouses
Storage platforms are often used in big data development
Data warehousing solution:
- Google BigQuery
- Amazon Redshift
- Snowflake
- Vertica
- Azure Synapse Analytics
Workflow planners
Workflow planning tools will help improve the data development process.
Automation and scheduling tools:
- Apache Airflow
- Luigi
- Apache Oozie
- Azkaban
Resource: Link
Business Value:
- Obtaining important information from large data sets, which in conclusion helps to make important decisions after data analysis.
- Data is directly linked to a wide range of business functions, from finance to sales.
- Cleaning large disparate data that can answer critical business questions.
- Preparation of information for further work of analysts, data processing specialists, and managers.
- High-quality management of business processes, thanks to processing and obtaining information from a large amount of data.
- Processing large amounts of data without overloading systems allows for scalability.
- Robustly built and optimized data architecture allows to prevent errors from occurring when working on large amounts of data.
- Companies with good data engineering practices can use their data to make better decisions and get a leg up on their competitors.
- Data engineering helps companies organize themselves more efficiently: it can handle as much volume as possible while still being cost-effective.
Use cases of Data Engineering
Diagnostic analytics is useful for any industry, as it can shed light on developing predictive models or creating successful business solutions.
Healthcare:
Data engineering in healthcare helps to acquire and integrate large volumes of patient data, such as electronic records (EHR), wearable devices, and medical imaging systems. With pipelines that ensure a seamless flow of data, healthcare providers gain access to patient information. In conclusion, this can improve patient care, provide quality predictive analytics, and aid in medical research.
Fintech:
Data engineering helps to collect and consolidate financial data from various sources. Aggregated transaction data, market data, and customer information are carried over data channels. It enables risk modeling, fraud detection, and investment analysis to help financial institutions make informed decisions and comply with regulatory requirements.
Manufacturing and supply chain:
Data Engineering will help your business optimize the supply chain by integrating data from sensors, RFID tags, and other sources. Channels can monitor the production process in real-time. This will allow companies to increase efficiency, minimize costs, and improve weak points.
E-commerce and personalization:
Support personalized shopping thanks to data engineering. Data Engineers at Amazinum will help you design data feeds and process transaction history data, click data, and customer behavior. This helps you fine-tune your referral mechanisms, which will help you attract customers and increase sales.
Energy and utilities:
Our Data Engineers will help you combine data from smart grids, sensors, and renewable energy sources to create reliable data pipelines. They will help you track and manage your energy consumption to optimize energy distribution, reduce your carbon footprint, and help optimize energy development.
Agriculture and farming:
Data Amazinum engineers will be able to integrate data from sensors, weather stations, and GPS devices to support and optimize agriculture. Conveyors created by our specialists will shed light on optimal sowing times, irrigation needs, and pest control. This will help farm owners to increase productivity and manage resources efficiently.
Transportation and logistics:
Amazinum engineers help you manage complex data streams from GPS trackers, in-vehicle sensors and supply chain partners. Thanks to this, you can optimize route planning, track shipments and take your business to the next level.
Amazinum Portfolio
Smart Eyes: The Integration of AI and ML in Surveillance Cameras for Enhanced Security
Beyond Monitoring: The Power of AI and ML in Construction Site Surveillance
Our Industry Focus
Our industry knowledge and background give our clients and partners confidence that we understand their business. Here we highlighted a few top industries we are good at, penetrating to the smallest details and nuances of a certain branch.
Partners & Clients
We deeply appreciate our partners for cooperation. Every member of the Amazinum Team does their best to provide the highest quality of services and solutions. We satisfy all your needs and requirements.
Technologies
Learn about technology stack we use to implement data science:
Programming languages:
for data analysis and processing:
for creating solutions:
for API development:
for visualization:
Deploying solutions:
Databases:
SQL:
NoSQL:
Cloud Solutions:
Vitaliy Fedorovych
CEO, Data Scientist at Amazinum
Hello there!
Amazinum Team assists you through all data science development processes:
from data collection to valuable insights generation.
Get in touch with our CEO and Data Scientist to figure out the next move together
- 4A Peremohy square, Ternopil, Ukraine, 46000
- +380 98 85 86 330
- vfedorovych@amazinum.com
Contact Us
Bazarnitska Alina
Client Partner
Ihor Khreptyk
Client Partner
Stopnyk Zoriana
Client Partner
Vitaliy Fedorovych
CEO, Data Scientist
