Data Engineering
Our data engineering services guarantee a comprehensive understanding of clients' data-related challenges.
Business benefits
Data Engineering Services
Who is a Data Engineer?
Data engineering plays a crucial role in designing and constructing systems for data collection, storage, and analysis across various industries. With skilled data engineers providing data access, conducting analysis, and building seamless pipelines to connect and transform data, businesses can make informed decisions and gain valuable insights. Datable's comprehensive data engineering services enable organizations to leverage advanced automated pipelines, propelling them to the next level of data utilization and automation. Discover the transformative potential of Datable's data solutions and experience the power of data-driven success for your business.
Development process
Our process
Industry expertise
Product Traceability System for a big manufacturing company
We helped JABIL a big electronic manufacturing company to build a complex Data Lake system based on AWS for Product Traceability.
Addepto Data Architects and data engineering experts have designed and implemented an end-to-end scalable system for fast analytical reporting and data storage.
Customer Data Platform implementation
Addepto team has supported the Custimy team with their data lake and analytics journey.
Our data engineering team has created a tailor-made data transformation layer for both structured data and Digital Marketing data sources, combined together in a single and unified cloud data warehouse.
Product Traceability System for a big manufacturing company
We helped JABIL a big electronic manufacturing company to build a complex Data Lake system based on AWS for Product Traceability.
Addepto Data Architects and data engineering experts have designed and implemented an end-to-end scalable system for fast analytical reporting and data storage. 
Customer Data Platform implementation
Addepto team has supported the Custimy team with their data lake and analytics journey.
Our data engineering team has created a tailor-made data transformation layer for both structured data and Digital Marketing data sources, combined together in a single and unified cloud data warehouse. Your industry isn't here? That’s not a problem!
Your industry isn't here?
That’s not a problem!
Technologies
Our array of Data Engineering Tools and Technologies
Our data engineering team is deeply committed to the open-source community and technology, ensuring clients don’t have to pay extra for some of the most popular data engineering software. At Datable, we use the most advanced tools and technology on the market, partnering with the largest cloud solution providers like AWS, Azure, and GCP to supply stable and high-quality software.
- Frameworks
- Software
Hadoop - is an open-source software framework that provides massive storage for any kind of data, enormous processing power, and the ability to handle virtually limitless concurrent tasks or jobs. It is designed for storing data and running applications on clusters of commodity hardware.
Apache Spark – Apache Spark is a data processing framework that can quickly perform tasks on large data sets. It can work alone but also distribute data processing across multiple computers.
Amazon Sagemaker – Amazon SageMaker is a machine learning service that enables data scientists and developers to speed up building and training machine learning models and directly deploy them into a production-ready hosted environment.
Kubeflow – Kubeflow is the open source machine learning toolkit on top of Kubernetes. It provides the cloud-native interface for your ML libraries, frameworks, pipelines and notebooks, interpreting stages in the created data science workflow into Kubernetes steps.
AutoKeras – AutoKeras is an open-source python package written in the deep learning library Keras. AutoKeras uses a variant of ENAS, an efficient and most recent version of Neural Architecture Search.
Key benefits
Our comprehensive, end-to-end data engineering solution is readily available to you, no matter where you are.
Data Pipelines
Our data engineering consulting team at Datable holds strong expertise in designing, constructing, and implementing end-to-end automated data pipelines of production quality, encompassing both on-premises and cloud-based solutions.
Data Preparation and ETL/ELT
Our Data Engineering team has developed data preparation, processing, and ETL/ELT (extract, transform, load) pipelines for multiple business departments, such as Finance, Sales, Supply Chain, and others. These pipelines facilitate data transformation and loading into the required data model for business reporting and advanced analytics.
Data Lake Implementation
With their cost-effectiveness and rapid processing capabilities, Data Lakes stand as the most potent and innovative choice for data storage. Implementing Data Lakes in your company can significantly enhance your business data architecture. Datable has effectively leveraged Data Lake solutions to address diverse client business challenges, including Product Traceability, Customer Data Platforms, IoT data reporting, and more.
Cloud Data Architecture
Our Data Architects can help your business get to the next level in terms of data analytics foundation by combining experience from several large enterprises. Today, it is essential to build and design flexible and highly accessible business data architectures. Don't miss the opportunity to explore our Big Data Engineering Services!
FAQ
How Do Big Tech Companies Leverage Data Engineering?
Many e-commerce giants use the power of data to create value for their businesses. Specific data allows you to attract potential customers and thereby significantly increase business profits. Nordstrom’s data engineers have developed a system for monitoring customer habits and behavior using Wi-Fi. The data obtained allowed the company to study the purchasing trends of its customers, which resulted in the optimization of personalized data and overall improved customer service. Data is being used by the company to optimize pricing, advertising, the supply chain, and even to decrease fraud. Amazon personalizes every interaction by using a large amount of client data.
Data Engineering vs. Data Science: What Sets Them Apart?
Data science and data engineering serve distinct roles in the data realm. Data science involves analyzing structured and unstructured data using statistics, applied science, and computer science to derive valuable insights for data-driven decision-making. On the other hand, data engineering is responsible for creating efficient data pipelines or procedures to transport data between instances, ensuring seamless data flow and accessibility for analysis. Together, they empower organizations to unlock the full potential of their data assets.
Is Data Engineering Necessary for Me?
Data engineering consulting is vital for businesses seeking to harness the power of data and improve performance. With experts’ guidance, companies can optimize data systems, derive valuable insights, and make data-driven decisions. Tailored solutions aligned with business objectives ensure streamlined processes and enhanced competitiveness in today’s data-rich landscape.
What is a Data Pipeline?
A data pipeline is a sequence of data processes that facilitate the extraction, processing, and loading of data from one system to another. It comes in two primary types: batch and real-time. Batch data pipelines process large volumes of data in chunks, making them suitable for non-time-sensitive tasks. On the other hand, real-time data pipelines handle data as it arrives, enabling near-instantaneous processing, ideal for time-sensitive applications and real-time decision-making. These data pipelines play a crucial role in managing and optimizing data movement, allowing businesses to leverage their data effectively for valuable insights and improved operations.
What does the future hold for Data Engineering?
The future of Data Engineering is promising, with continuous advancements in technology and increasing data complexities. As businesses gather and utilize more data, the demand for efficient data engineering solutions will grow. We can expect further automation, optimization, and scalability in data pipelines to accommodate large datasets and real-time processing. Additionally, Data Engineering will play a vital role in supporting the development of AI and machine learning applications, driving innovation and business success.
What does a Data Engineer do?
A Data Engineer is responsible for designing, constructing, and maintaining data pipelines and systems that enable the seamless flow of data between different stages of processing. They work with various data sources, ensuring data is extracted, transformed, and loaded efficiently to support data-driven decision-making and analysis. Data Engineers also play a crucial role in data infrastructure design and optimization, ensuring data accessibility and scalability. Their expertise is vital in organizing and managing data, enabling businesses to derive valuable insights and improve overall data operations.
Why is Data Engineering so important?
In the realm of data management, a Data Engineer holds a pivotal role. They are tasked with designing, constructing, and maintaining data pipelines and systems, ensuring a smooth flow of data through various processing stages. Working with diverse data sources, Data Engineers handle data extraction, transformation, and loading to facilitate data-driven decision-making and analysis. Additionally, their expertise extends to optimizing data infrastructure, ensuring accessibility and scalability for efficient data management. With their crucial contributions, businesses can leverage data insights effectively, fostering improved data operations and overall success.
Check out our blog and make sure you are keeping up with the last trends in your industry
Data Science vs. Data Engineering
What are the main tools and programming languages used in data engineering and data science?
Find out the main difference between these two fields.
Who is a Data Engineer?
The profession of Data Engineer was ranked as the fastest-growing tech job in 2019!
With the number of open positions up 50% compared to last year.
Data Lake Architecture
In this article, you can learn more about Data Lake Architecture.
Learn what are the key factors you should keep in mind when planning Data Lake Architecture.
We are a fast-growing company with the trust of international corporations
Addepto on the list of top 10 AI consulting companies by Forbes.
We are proud to be among the top BI & Big Data Consultants in Los Angeles on Clutch
We are proud to be among the top BI & Big Data Consultants in Los Angeles on Clutch
We are proud to be among the top BI & Big Data Consultants in Los Angeles on Clutch
We are proud to be among the top BI & Big Data Consultants in Los Angeles on Clutch
Our clients
Glossary
FAQ
How Do Big Tech Companies Leverage Data Engineering?
Many e-commerce giants use the power of data to create value for their businesses. Specific data allows you to attract potential customers and thereby significantly increase business profits. Nordstrom’s data engineers have developed a system for monitoring customer habits and behavior using Wi-Fi. The data obtained allowed the company to study the purchasing trends of its customers, which resulted in the optimization of personalized data and overall improved customer service. Data is being used by the company to optimize pricing, advertising, the supply chain, and even to decrease fraud. Amazon personalizes every interaction by using a large amount of client data.
Data Engineering vs. Data Science: What Sets Them Apart?
Data science and data engineering serve distinct roles in the data realm. Data science involves analyzing structured and unstructured data using statistics, applied science, and computer science to derive valuable insights for data-driven decision-making. On the other hand, data engineering is responsible for creating efficient data pipelines or procedures to transport data between instances, ensuring seamless data flow and accessibility for analysis. Together, they empower organizations to unlock the full potential of their data assets.
Is Data Engineering Necessary for Me?
Data engineering consulting is vital for businesses seeking to harness the power of data and improve performance. With experts’ guidance, companies can optimize data systems, derive valuable insights, and make data-driven decisions. Tailored solutions aligned with business objectives ensure streamlined processes and enhanced competitiveness in today’s data-rich landscape.
What is a Data Pipeline?
A data pipeline is a sequence of data processes that facilitate the extraction, processing, and loading of data from one system to another. It comes in two primary types: batch and real-time. Batch data pipelines process large volumes of data in chunks, making them suitable for non-time-sensitive tasks. On the other hand, real-time data pipelines handle data as it arrives, enabling near-instantaneous processing, ideal for time-sensitive applications and real-time decision-making. These data pipelines play a crucial role in managing and optimizing data movement, allowing businesses to leverage their data effectively for valuable insights and improved operations.
What does the future hold for Data Engineering?
The future of Data Engineering is promising, with continuous advancements in technology and increasing data complexities. As businesses gather and utilize more data, the demand for efficient data engineering solutions will grow. We can expect further automation, optimization, and scalability in data pipelines to accommodate large datasets and real-time processing. Additionally, Data Engineering will play a vital role in supporting the development of AI and machine learning applications, driving innovation and business success.
What does a Data Engineer do?
A Data Engineer is responsible for designing, constructing, and maintaining data pipelines and systems that enable the seamless flow of data between different stages of processing. They work with various data sources, ensuring data is extracted, transformed, and loaded efficiently to support data-driven decision-making and analysis. Data Engineers also play a crucial role in data infrastructure design and optimization, ensuring data accessibility and scalability. Their expertise is vital in organizing and managing data, enabling businesses to derive valuable insights and improve overall data operations.
Why is Data Engineering so important?
In the realm of data management, a Data Engineer holds a pivotal role. They are tasked with designing, constructing, and maintaining data pipelines and systems, ensuring a smooth flow of data through various processing stages. Working with diverse data sources, Data Engineers handle data extraction, transformation, and loading to facilitate data-driven decision-making and analysis. Additionally, their expertise extends to optimizing data infrastructure, ensuring accessibility and scalability for efficient data management. With their crucial contributions, businesses can leverage data insights effectively, fostering improved data operations and overall success.
Check out our blog and make sure you are keeping up with the last trends in your industry
Data Science vs. Data Engineering
What are the main tools and programming languages used in data engineering and data science?
Find out the main difference between these two fields.
Who is a Data Engineer?
The profession of Data Engineer was ranked as the fastest-growing tech job in 2019!
With the number of open positions up 50% compared to last year.
Data Lake Architecture
In this article, you can learn more about Data Lake Architecture.
Learn what are the key factors you should keep in mind when planning Data Lake Architecture.
How Do Big Tech Companies Leverage Data Engineering?
Many e-commerce giants use the power of data to create value for their businesses. Specific data allows you to attract potential customers and thereby significantly increase business profits. Nordstrom’s data engineers have developed a system for monitoring customer habits and behavior using Wi-Fi. The data obtained allowed the company to study the purchasing trends of its customers, which resulted in the optimization of personalized data and overall improved customer service. Data is being used by the company to optimize pricing, advertising, the supply chain, and even to decrease fraud. Amazon personalizes every interaction by using a large amount of client data.
Data Engineering vs. Data Science: What Sets Them Apart?
Data science and data engineering serve distinct roles in the data realm. Data science involves analyzing structured and unstructured data using statistics, applied science, and computer science to derive valuable insights for data-driven decision-making. On the other hand, data engineering is responsible for creating efficient data pipelines or procedures to transport data between instances, ensuring seamless data flow and accessibility for analysis. Together, they empower organizations to unlock the full potential of their data assets.
Is Data Engineering Necessary for Me?
Data engineering consulting is vital for businesses seeking to harness the power of data and improve performance. With experts’ guidance, companies can optimize data systems, derive valuable insights, and make data-driven decisions. Tailored solutions aligned with business objectives ensure streamlined processes and enhanced competitiveness in today’s data-rich landscape.
What is a Data Pipeline?
A data pipeline is a sequence of data processes that facilitate the extraction, processing, and loading of data from one system to another. It comes in two primary types: batch and real-time. Batch data pipelines process large volumes of data in chunks, making them suitable for non-time-sensitive tasks. On the other hand, real-time data pipelines handle data as it arrives, enabling near-instantaneous processing, ideal for time-sensitive applications and real-time decision-making. These data pipelines play a crucial role in managing and optimizing data movement, allowing businesses to leverage their data effectively for valuable insights and improved operations.
What does the future hold for Data Engineering?
The future of Data Engineering is promising, with continuous advancements in technology and increasing data complexities. As businesses gather and utilize more data, the demand for efficient data engineering solutions will grow. We can expect further automation, optimization, and scalability in data pipelines to accommodate large datasets and real-time processing. Additionally, Data Engineering will play a vital role in supporting the development of AI and machine learning applications, driving innovation and business success.
What does a Data Engineer do?
A Data Engineer is responsible for designing, constructing, and maintaining data pipelines and systems that enable the seamless flow of data between different stages of processing. They work with various data sources, ensuring data is extracted, transformed, and loaded efficiently to support data-driven decision-making and analysis. Data Engineers also play a crucial role in data infrastructure design and optimization, ensuring data accessibility and scalability. Their expertise is vital in organizing and managing data, enabling businesses to derive valuable insights and improve overall data operations.
Why is Data Engineering so important?
In the realm of data management, a Data Engineer holds a pivotal role. They are tasked with designing, constructing, and maintaining data pipelines and systems, ensuring a smooth flow of data through various processing stages. Working with diverse data sources, Data Engineers handle data extraction, transformation, and loading to facilitate data-driven decision-making and analysis. Additionally, their expertise extends to optimizing data infrastructure, ensuring accessibility and scalability for efficient data management. With their crucial contributions, businesses can leverage data insights effectively, fostering improved data operations and overall success.
Check out our blog and make sure you are keeping up with the last trends in your industry
Data Science vs. Data Engineering
What are the main tools and programming languages used in data engineering and data science?
Find out the main difference between these two fields.
Who is a Data Engineer?
The profession of Data Engineer was ranked as the fastest-growing tech job in 2019!
With the number of open positions up 50% compared to last year.
Data Lake Architecture
In this article, you can learn more about Data Lake Architecture.
Learn what are the key factors you should keep in mind when planning Data Lake Architecture.
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Addepto offered an individual approach to our needs and high-tech solutions that will be efficient in the long term. They conducted a detailed analysis and were open to trying out innovative ideas.
Przemysław Piekarz Sales Analysis Manager – InPost