Data Engineering

Architect your success

From Data to Ta Da!

Experience the magic of data engineering

Let Your Data Drive Decisions

Too many organizations and leaders give too much weight to “gut instinct” in their decision making process. Intuition is limited, prone to bias, and leads to suboptimal decisions and failure. To make the best decisions about sales, procurement, operations, and even your hiring, you must have accurate, up-to-date information that informs your choices. It's easy to see the enormous impact this would make on your top and bottom lines. Better budgeting, improved sales numbers, less wastage… This is data engineering at its best, and SPAR helps you achieve it.

Go from Clutter to Clarity​

The problems that complicate decision-making can be summarized in one word: VUCA. VUCA, which stands for volatility, uncertainty, complexity, and ambiguity, makes a situation difficult to analyze and respond to. Volatile market conditions, uncertain outcomes, situational complexity, and ambiguity can cripple decision-making. Data too is shackled by the same constraints: The volume of data generated by any organization is enormous, diverse, uncertain, complex, and ambiguous. Understanding how to resolve these issues is the key to better outcomes.

Let Your Data Drive Decisions

Too many organizations and leaders give too much weight to “gut instinct” in their decision making process. Intuition is limited, prone to bias, and leads to suboptimal decisions and failure. To make the best decisions about sales, procurement, operations, and even your hiring, you must have accurate, up-to-date information that informs your choices. It’s easy to see the enormous impact this would make on your top and bottom lines. Better budgeting, improved sales numbers, less wastage… This is data engineering at its best, and SPAR helps you achieve it.

Go from Clutter to Clarity

The problems that complicate decision-making can be summarized in one word: VUCA. VUCA, which stands for volatility, uncertainty, complexity, and ambiguity, makes a situation difficult to analyze and respond to. Volatile market conditions, uncertain outcomes, situational complexity, and ambiguity can cripple decision-making. Data too is shackled by the same constraints: The volume of data generated by any organization is enormous, diverse, uncertain, complex, and ambiguous. Understanding how to resolve these issues is the key to better outcomes.

How It Works

extract the data

Extract​

The first step in utilizing the power in your data is to extract the data — structured and unstructured — from a variety of sources, including SQL & No SQL servers, CRMs, ERPs, email, webpages, and other files. Our data engineering teams will extract this data for further processing or storage​

Transform

Transform

This is the staging area where raw data is transformed and consolidated in preparation for analytical use cases. During this phase, data is filtered, cleaned, and authenticated. In the interests of consistency, wherever necessary, the raw data is converted for unity. This includes currency conversion, units of measurement, and changing headers of rows and columns. Data that is required by industry or government regulatory compliance is protected. Finally, the data is formatted to align with the schema of the target data warehouse.​

Load

Load

This is the final step, in which the transformed data is migrated to a target data warehouse. This, typically, begins with a full transfer of all data, followed by periodic loading of incremental data changes and, as needed, full refreshes that completely replace all the data in the warehouse. Our ETL process is largely automated and takes place during off-peak hours when your source systems and the data warehouse experience the lowest traffic. Occasionally, when the data involves high-volume, unstructured datasets, loading happens directly from the source. This is known as ELT; it is particularly well-suited for big data management.​

Data Capture

Extract

The first step in utilizing the power in your data is to extract the data — structured and unstructured — from a variety of sources, including SQL & No SQL servers, CRMs, ERPs, email, webpages, and other files. Our data engineering teams will extract this data for further processing or storage​
Transform

Transform

This is the staging area where raw data is transformed and consolidated in preparation for analytical use cases. During this phase, data is filtered, cleaned, and authenticated. In the interests of consistency, wherever necessary, the raw data is converted for unity. This includes currency conversion, units of measurement, and changing headers of rows and columns. Data that is required by industry or government regulatory compliance is protected. Finally, the data is formatted to align with the schema of the target data warehouse.​
Load

Load

This is the final step, in which the transformed data is migrated to a target data warehouse. This, typically, begins with a full transfer of all data, followed by periodic loading of incremental data changes and, as needed, full refreshes that completely replace all the data in the warehouse. Our ETL process is largely automated and takes place during off-peak hours when your source systems and the data warehouse experience the lowest traffic. Occasionally, when the data involves high-volume, unstructured datasets, loading happens directly from the source. This is known as ELT; it is particularly well-suited for big data management.​

Take the Next Step

Think of data as information. We all know information is power but that’s true only when you know how to use it. And this is precisely what a good data engineering solution does for you. Data management is more of a must-have than a nice-to-have solution—especially when times are tough, budgets tighter and ROI harder to achieve. Failure to recognize the advantages that data management provides leaves you with inefficient tools, raises costs, and a shrinking market position. Let SPAR help. Reach out today for a no-commitment presentation and learn how we build reliable, data-engineered products that make your business objectives easier to achieve.
Contact Us

Trusted Across Industries

FAQ

  • All businesses. Because data-driven decision-making offers sustainable advantages for businesses across industries, from manufacturing to philanthropy.
  • Data scientists: Because they need access to reliable data—or all types
  • IT teams. Because delivering reliable experiences is part of their mandate
  • Security teams: Because data makes compliance easier to monitor and
  • Source control and process automation that makes it simpler and more economical to deliver and maintain your code
  • Constant feedback so you can monitor progress and assure yourself that the development is in line with your business needs
  • Elimination of human error, through automation
  • Easier monitoring, logging, and operational visibility
  • Real world experience and expertise across data technologies

Take the Next Step

Think of data as information. We all know information is power but that’s true only when you know how to use it. And this is precisely what a good data engineering solution does for you. Data management is more of a must-have than a nice-to-have solution—especially when times are tough, budgets tighter and ROI harder to achieve. Failure to recognize the advantages that data management provides leaves you with inefficient tools, raises costs, and a shrinking market position. Let SPAR help. Reach out today for a no-commitment presentation and learn how we build reliable, data-engineered products that make your business objectives easier to achieve.
Contact Us