A data engineer is responsible for the ingestion, quality, integration, governance, and security of data – in short, taking data from various sources and making it reliable and useful. This enables data analysts and data scientists to use the data for business intelligence, data analytics, and building data products.
Many organizations lack the specialized data engineering expertise that is required to fully realize value from data. At Infostrux, we handle the data engineering for you. We build and manage automated data pipelines as a unified data cloud solution running on Snowflake.
Today’s data landscape requires significant domain knowledge to navigate. Businesses are striving to become increasingly data-driven, but investments are typically directed at the visible part of the iceberg — data analysis and data science, where value is realized through business intelligence practices. This often comes at the detriment of the data engineering posture of the organization, leading to a very robust business intelligence platform generating wrong insights from bad data with high confidence.
This is where we come in. We take on the many undifferentiated challenges of data engineering and deliver curated, refined data that your team can then use to generate significant, reliable value for your business at an accelerated pace.
Businesses face many common problems which can lead BI, data analytics, and data science projects to fail:
- Organizational silos produce data silos preventing value realization; we break them down
- Too much effort is spent on fixing integration failures before producing insights; we take them out of the equation
- BI projects fall into the ‘high confidence bad answers’ trap due to unreliable data; we deliver certified data sets
Whether your data strategy is to drive growth, reduce costs, or mitigate risks, we can help. From medium-sized businesses with few data sources to large enterprises facing big data challenges, our team of data engineers can deliver data you can trust.
Our first step is to set up a discovery call. This is where we can learn more about your business’s specific needs, challenges, and goals. This also gives you the opportunity to ask any questions you may have and learn more about what we do.
Next, we need to dive further into the details of your domain to fully assess and diagnose your business requirements. During this stage, we will meet with your data team and review your technology stack.
Once we have gathered enough information from the call and our audit, we can put together a plan that’s right for you. We will deliver a detailed proposal, go over any questions you may have, and make any necessary revisions.
Upon approval, we will get to work. Depending on the package, you can expect our team to complete our services in as little as 4 – 6 weeks for a pilot (MVP) engagement, 2 – 4 months for a foundational engagement, and 4 – 6 months for a large-scale migration engagement.
We also offer ongoing monitoring, maintenance, and optimizations so as your business grows and evolves, we will be there to support you.
We offer the following services:
Data Engineering – Our teams build automated data pipelines to ingest data from a variety of structured, semi-structured, and unstructured data sources, integrate the data into a unified data warehouse/data lake solution, and engineer solutions for ongoing data validation, quality, and governance.
Data Architecture – Our data architects work alongside our customers as consultants in analyzing, designing, prototyping, and implementing data warehousing, data lake, and data analytics solutions on top of Snowflake’s Data Cloud Platform.
Data Analytics Implementation – We work with our customers to implement BI reporting and analytics solutions on top of Snowflake, and provide assistance in developing reports and dashboards as well as optimize their performance and cost.
Data Science Support – Our teams build data lakes and implement appropriate technologies to enable access as well as provide compute capacity for our customers’ data science teams to perform data analysis, modelling, and training of machine learning models.
Managed Data Cloud – We deliver automated data pipelines and validated data sets as a unified and managed solution offered as service, which includes ongoing monitoring, maintenance, and optimizations.
Our pricing depends on a variety of factors, including number of data sources, the complexity of integration, and services required. To determine a price, we require an initial discovery call and a further audit to understand the specific needs of your business.
The nature of the work Infostrux undertakes regularly involves access to sensitive customer data. As such, security and regulatory compliance is at the forefront of Infostrux’s internal policies and engagement model. The three core pillars of Infostrux’s security and compliance strategy are:
- Internal Regulation
All data access and customer engagement processes are internally documented and rigorously audited on a monthly basis. All Infostrux employees and contractors are subjected to criminal, credit, and professional background checks and undergo security awareness training before being granted access to any customer asset. Any access to customer assets is limited to least privilege, need-to-know principles. A company-wide risk register is maintained and reviewed monthly to ensure continued vigilance and proactivity as part of Infostrux operations. Infostrux aims to achieve its SOC2 compliance certification by early 2022.
- External Accountability
To help establish its SOC2-compliant operational framework and externally audit its continued compliance, Infostrux has mandated the ongoing services of a third-party security firm specialized in governance and compliance. The firm provides external validation to process reviewing activities and tests the established compliance framework on a quarterly basis. Infostrux also leverages a third-party application security consulting firm to test strategic IP architecture.
- Risk Mitigation
Due to the sensitive nature of its customers’ data, Infostrux considers the mitigation of the risk of any potential data breach as its highest priority. As such, proactive mitigation strategies are enacted throughout the Infostrux delivery model to limit data exposure to an absolute minimum. All sensitive development work for the customer is accomplished through Virtual Desktop Infrastructure (VDI) hosted within the customer’s environment and accessed by Infostrux resources through named, single-user VPN connections. This has the immediate effect of ensuring that no customer data ever leaves the premises of its infrastructure and allows for any customer-mandated controls to be put in place as required by their internal governance protocols.
Snowflake is a data warehouse cloud platform that supports large-scale data and productivity. In 2020, Snowflake became a public company having the largest software IPO in history, raising $3.4 billion dollars. The platform is currently used by over 3,400 customers in 65 countries, exhibits a significant increase in year-over-year adoption velocity, and holds the second-largest market share in the data warehousing category.
Snowflake uses a pay-as-you-go model, enabling customers to use the data storage they need and become more data-driven. Snowflake also uses SQL, which is a common programming language that most organizations understand. This democratizes data storage, enabling customers to focus on data and not infrastructure.
To learn more about Snowflake, please see our blog post Why Did We Choose Snowflake?
The SnowPro Certification is an exam that requires individuals to demonstrate their knowledge and to apply specific core expertise implementing and migrating to Snowflake. Being SnowPro Certified means the individual has a thorough understanding of Snowflake as a cloud data platform and has the knowledge necessary to design, develop, and manage secure, scalable Snowflake solutions to drive business objectives.
For more information, please see Snowflake Certification.
Sharing datasets across departments within an organization can have tremendous value. For example, marketing data is often used to understand sales data, but this data is only useful if it is reliable.
Organizations can establish trusted and reliable datasets, which is akin to having a verification stamp telling the analysts that the data is trusted and authoritative. Once a dataset is certified, it becomes available for analysts across an organization to find, access, and work with to create their reports and dashboards.
ETL (Extract, Transform, and Load) is a process in which different kinds of data are collected from multiple sources and consolidated into a single location – a data warehouse. An ETL data pipeline can handle a large and complex volume of data, playing a critical role in data integration and data management strategies, as well as business intelligence.
Our teams are organized into pods, combining project delivery, data architecture, and data engineering experience on every customer project. This allows our experienced team to combine our diverse skill sets and collaborate closely with our customers’ business analyst, data architecture, and data science teams.
DataOps is a way to manage your entire data infrastructure through code. This data automation includes schemas, data, testing, and all the orchestration around them in an easily manageable, fully auditable package including governance. Andy Palmer—the person that popularized the term, said this:
“DataOps is a data management method that emphasizes communication, collaboration, integration, automation, and measurement of cooperation between data engineers, data scientists and other data professionals.”
DataOps is a culturally-focused transformation; it is about democratizing data and using agile, collaborative methods to increase data usage while making it more reliable. This is important because much of the big data projects’ problems are due to bad data.
To learn more, please see our blog post The Data Ops Revolution
Data architecture refers to the structure of your data – data quality, data collection, data storage, data integration, how data is processed, and how data is utilized in an information system. Composed of models, rules, and policies, data architecture provides a set of standards for the design and development of data flows in a system. Data integration, for example, relies on a set of data architecture standards that dictate the interaction between two or more data systems.