Data Engineering

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

Data Engineering title

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: 

Extract — retrieving raw data from numerous sources — databases, APIs, files, etc.
Transform — standardizing data to meet the format requirements.
Load — saving data to a new destination (typically a data warehouse).

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.

ETL pipeline

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
state of data ingineering

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.

Content:

Amazinum Portfolio

Check out a few of our recent projects
Navigating the Future: Harnessing AI and ML in the Maritime Industry
harnessing ai, graph
re-identification graph

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.

SEO icon
SEO & Advertising
Healthcare icon
Healthcare
Security icon
Safety & Security
Sport icon
Sport
E-commerce logo
E-commerce & Retail
Gambling icon
Gambling and Casino
Technolodgy icon
Technology (Localisation)

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.

byteant logo
sleepnumber logo
ushealth
wework
peiko
macys
softserve logo
aid genomics logo
startupsoft

Technologies

Learn about technology stack we use to implement data science:

Programming languages:

Python logo

for data analysis and processing:

OpenCV logo
NumPy logo
SciPy logo
Pandas logo

for creating solutions:

spaCy logo
Spark logo
PyTorch logo
TensorFlow logo
Keras logo
Scikit-Learn logo

for API development:

RabbitMQ logo
Flask logo
FastAPI logo
Django logo

for visualization:

ploty logo
matplotlib logo

Deploying solutions:

Docker logo
GitLab logo
TensorFlow logo
DVC logo
Vertex.ai logo
KubeFlow logo

Databases:

SQL:

PostgreSQL logo
ClickHouse logo
BigQuery logo
MySQL logo

NoSQL:

Bigtable logo
Elastic logo
Cassandra logo
mongo DB logo

Cloud Solutions:

Google Cloud logo
AWS logo

Vitaliy Fedorovych

CEO, Data Scientist at Amazinum

Vitaliy Fedorovych contact us photo

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

Contact Us

Click or drag a file to this area to upload.
Alina Bazarnitska contact us photo

Bazarnitska Alina

Client Partner

Ihor Khreptyk contact us photo

Ihor Khreptyk

Client Partner

Stopnyk Zoriana photo

Stopnyk Zoriana

Client Partner

Vitaliy Fedorovych contact us photo

Vitaliy Fedorovych

CEO, Data Scientist

Vitaliy Fedorovych

CEO, Data Scientist at Amazinum

Vitaliy Fedorovych contact us photo

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

Contact Us

Click or drag a file to this area to upload.

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