Why is Data Management important? Discover the benefits your business can enjoy
Read our article to discover and explore why data management is important and how you can make it work for your company.
Extract. Transform. Load.
To let you experience the power of data.
In today's digital world, just having information is not enough. What's essential is the way you use your data. This is where data engineering comes into play. Data engineering takes control of collecting, sorting, and storing the required amounts of data. The thoughtfulness and meticulousness of this process allows businesses to have constant access to the data they need and it gives them the confidence that it's all up-to-date, correct, and reliable.
Microservices oriented architectures
When you need to provide analysts and quality engineering teams with reliable data, it must be properly processed, handled, and presented in an accessible format.
When you have multiple data sources and want to organize them more clearly and conveniently, Solvd can assist you with transforming and integrating this data into a usable format.
When your data contains sensitive information about your business's inner processes and/or customers. In these cases, it's crucial to implement all of the necessary data security and compliance measures in order to prevent data leakage and disclosure.
When the decision-making process has bottlenecks or lacks information due to poor data engineering processes. We allow businesses to overcome their information gaps by providing the appropriate tools and infrastructure to access, process, and analyze data.
When your massive amount of data can't be efficiently handled and used with legacy databases and tools. Data modernization is an effective way to seamlessly migrate away from an existing outdated system to a cloud storage infrastructure or new target platforms.
When your company spends a lot of resources on manual ETL processes, data engineering is a service that can automate these processes. This automation reduces both time wasted and human errors.
A multi-stage process of moving away from on-prem legacy systems towards advanced and more robust cloud-based storage. The result is actionable insights, improved decision-making processes, regulatory compliance, and increased efficiency.
Data ingestion lets teams stay ahead of the curve! The first step is extracting structured or unstructured raw data from various batch and stream processing sources. Next is data preparation, where this data is cleaned, refined, and converted into a format available for a particular cloud system or legacy database.
Continuous input, ongoing processing, and consistent output enables enterprises to proactively find solutions, streamline operations, and personalize the user experience. Solvd implements batch and real-time processing systems in distributed environments based on cloud, web hosting, and mobile services.
Data-intensive applications are typically built around several core functionalities and require fast and seamless data exchange. At Solvd, data engineers employ caches, search indexes, stream and batch processing, and other solutions to make data-intensive apps operate smoothly and efficiently.
Implementation of efficient production pipelines based on database objects, application data, infrastructure-as-code artifacts, as well as data validation and transformation logics, both in cloud-based and legacy deployment services. Data CI/CD practices are the key to faster time-to-insights, scalability, and reduced errors.
End-to-end data pipelines are the backbone of data processing from the source to the final dashboards. It enables companies to save time, ensure consistency and standardization in data processing, and keep the workflow transparent. We build independent and replayable data workflow pipelines using various legacy, big data, cloud orchestration, and data management pipeline tools and techniques.
The ELT series of operations simplifies and speeds up the process of entering, manipulating, and converting data into a usable format. It also helps businesses retain large volumes of information securely, and it also makes it accessible and searchable for future use. This storage can be in traditional relational databases, NoSQL databases, data lakes, or cloud-based storage solutions.
Get the most at each stage
Gathering the requirements of data sources, types, and purposes. At this stage, we also determine the needs and at what delay the data is needed.
Designing the pipeline based on the requirements and building a plan to develop it with the correct ETA and analyzing the solution in order to optimize the cost of implementation.
Transformation of the data into a suitable format for storage and analysis. If needed, we extract the data into a raw layer that can help processes down the road get down to only one source. This can be known as Data Lake.
Building and implementing the storage solution that meets our client's needs. The next step is implementing the proper Data Monitoring tools.
Providing data analysts and data scientists with data access through data visualization and SQL queries. Solvd's team offers suggestions on the data that we find odd and suspicious.
Automation and maintenance of ongoing data collection, processing, and storage.
Every business has its specific objectives, requirements, strategy, and resources. We understand that and, in turn, we offer various cooperation options. Staff augmentation, dedicated team, managed services, outsourcing, or consulting — choose the option that best fits your individual needs.
The more information you have, the more criteria you can consider when making a decision. Get limitless access to invaluable data that helps you build an insightful self-service analytic model in order to improve your decision-making process.
The power of data engineering services enables businesses to uncover up-to-date information about the market, its tendencies, and its development direction. Deep analytics of this data lets them predict trends and swiftly respond to market dynamics. This all makes it easier to meet customers' needs.
Data-driven decisions, personalized marketing strategies, precise segmentation and targeting, real-time analytics, and an optimized customer journey lead companies to increased click-through rates, conversions, and sales.
Employing operational data and having access to distributor data helps companies improve supply chain visibility, illuminate gaps, and enhance replenishment efficiency.
By leveraging our frameworks and accelerators, you'll streamline data-related processes, improve collaboration, and speed up prototyping. This all results in a faster time-to-market without compromising on quality.