Data Engineering

Data Engineering
The data engineering field is enormous, ranging from raw data engineering and collecting primitive signals to pipeline and even data analytics engineering. It takes time, expertise, and lots of dedication to identify the right technologies and tools, architect the data structure and models, plan the roadmap, and implement strategies.
Having a reliable set of data engineering flows is indispensable, and you can't trust your data otherwise.
1. Data Engineering

Data Pipeline Architecture and Implementation

Data pipelines are one of the most critical parts of the data infrastructure. They enable data movement from source systems to destination locations, from databases to data warehouses, from APIs to data lakes, and so on. Without a proper architecture, robust monitoring, and strict SLAs, pipelines can quickly become a point of failure. Hence, data processing pipelines require careful analysis, design, and consideration.
There are different types and techniques of data pipelining. Some are built purely for data extraction and transfer, and other pipelines are also responsible for data deduplication, filtration, and transformation. There are two primary categories of full-scale pipelines: ETL, which stands for Extract, Transform and Load, and ELT, which stands for Extract Load and Transform.
The former is primarily used to put adequately formatted data into structured storage. The latter is used to set the data into storage in its raw form and transform it later. One argument, however, always stays the same: data quality will suffer without a reliability system in place.
Our favorite tools for ETL
FivetranStitchMeltanoAWS GluePython
2. Data Engineering

Data Mesh Architecture

Data Mesh is a unique approach to infrastructure organization and data management. The primary assumption is that instead of building an ever-growing data silo that combines data from all possible sources, it follows a different path of the separated set of smaller nodes responsible for data ingestion, transformation, storage, and presentation. The decentralized strategy of data mesh distributes data ownership to domain-specific teams that manage, own, and serve the data as a product.
With a domain-based nature and a data as a product approach, the team allocated to a specific node of the data mesh can indeed be an expert on the data from top to bottom. That significantly increases the reliability of the node since, in case of an issue, there are no dependencies on the other teams/departments within a company, and the team can quickly solve it.
3. Data Engineering

Design and Build of Data Lake and Data Warehouse

Both data warehouses and data lakes have their application on various projects. Sometimes even simultaneously. Data lakes, in their nature, are simply pools of data coming in various formats from various sources. Data mining by data scientists or engineers is a primary use case for such data. Sometimes data lakes can store more structured information, making them a cost-efficient solution for long-term storage
For the data warehouse, the situation is a bit different. Carefully selecting, designing, and building a data warehouse goes a long way. Data modeling plays an essential role in the future of data representation, business reporting, and data apps built on top of the data warehouse.
Warehouse solutions we love
FireboltGoogle Big QueryAmazon RedshiftSnowflake
4. Data Engineering

Design and Build Data Tables & Data View

Most data warehousing solutions rely on a simple concept, a table. Tables are a versatile and comprehensive way of working with the data and provide a globally understandable interface for data representation. Table structure and design matter not only in technical terms, like storage efficiency, performance, and scalability, but also in business terms, like the ease of use, data transparency, logical data modeling, etc. One important concept that is sometimes overlooked is data view.
A simple yet effective way to represent data in a different format, even transform the data to a different model without creating a separate table. Data views allow creating a portal to the existing table, often even combining data from multiple tables simultaneously. At first glance, this simple feature might seem futile, but on a closer look, it becomes clear that it packs a serious punch in regards to the functionality it gives.
The best technology for building logical views
Dbtlooker
5. Data Engineering

Raw Data Governance

Working with datasets is one of the most critical parts of any data-related process. Properly organizing data collection effort, working on a carefully designed data plan tailored to a specific project or need, creating extensive data plan documentation and implementation instructions for other team members, and carrying out the implementation with the QA process. All of that contributes to the final picture: reliable ingested data that can be safely used for analysis. Without this, no data can be trusted.
Important topics for raw data governance are privacy, data security, data quality and compliance with all regulatory requirements. Making sure that data is collected properly and in accordance with all rules and regulations is as important as the data itself.
We understand the intricacies of GDPR, CCPA, and other regulations. We have experience building automated solutions for Right of Know/Right to be Informed and Right to be Forgotten/Right of Deletion.
6. Data Engineering

Data OPS

DataOps (DATA Operations) is a concept and set of practices for continuous data integration between processes, teams and systems to improve the efficiency of corporate governance or industry interaction through distributed collection, centralized analytics and flexible information access policies taking into account its confidentiality, restrictions on use and compliance with integrity.
DataOps Engineers create and implement the processes that enable successful teamwork within the data organization. They design the orchestrations that enable work to flow seamlessly from development to production.