Troops’ mission is to make business software human, so work gets easier, information improves, teams sell more, and business grows. Troops revenue communications software improves CRM data quality, automates administrative work, enhances visibility, and enables collaboration. Troops works with world-class brands including Slack, Twilio, DoorDash, and Stripe to improve forecast accuracy, organizational transparency, and win rates.
October 2018 – May 2020
Support all infrastructure and environment modernization (both non-prod and production environments) and provide much-needed resources to allow Troops’ in-house staff to concentrate on product development.
Caylent is an incredibly experienced and knowledgeable company. Their team helped us get up to speed with all AWS practices and quickly roll out Kubernetes.
– Greg Ratner, CTO @ Troops.ai
Troops was seeking a DevOps partner with the expertise to manage day-to-day infrastructure administration and provide support for the Troops development team in accelerating product development and releases. The Troops team was looking for a broad scope of support, skills, and development expertise that made hiring an additional in-house team member challenging and expensive. The company had accrued a backlog of technical debt that they knew needed to be paid down while allowing internal staff to focus on value generation surrounding product innovation and delivery. Caylent’s deep domain expertise in Cloud Native and AWS offered a clear value proposition to free up precious development resources from time-consuming infrastructure management and provided operational enhancements in the form of best practice advice, skills, and tools.
#case studies #aws #case study #troops
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
We are a top-rated software quality assurance & testing company leveraging our potential to profound expertise in delivering quality tested applications to businesses.
In the past 16 years, we have delivered over 4200 quality-assured software to a global clientele catering to various industries such as healthcare, adtechs, eLearning, and more.
Planning to outsource software QA services? Or would you like to hire an offshore software testing team?
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Data quality is top of mind for every data professional — and for good reason. Bad data****costs companies valuable time, resources, and most of all, revenue. So why are so many of us struggling with trusting our data? Isn’t there a better way?
The data landscape is constantly evolving, creating new opportunities for richer insights at every turn. Data sources old and new mingle in the same data lakes and warehouses, and there are vendors to serve your every need, from helping you build better data catalogs to generating mouthwatering visualizations (leave it to the NYT to make mortgages look sexy).
Not surprisingly, one of the most common questions customers ask me is “what data tools do you recommend?”
More data means more insight into your business. At the same time, more data introduces a heightened risk of errors and uncertainty. It’s no wonder data leaders are scrambling to purchase solutions and build teams that both empower smarter decision making and manage data’s inherent complexities.
But I think it’s worth asking ourselves a slightly different question. Instead, consider: **“what is required for our organization to make the best use of — and trust — our data?”**
It’s a scary prospect to make decisions with data you can’t trust, and yet it’s an all-too-common practice of even the most competent and experienced data teams. Many teams first look to data quality as an anecdote for data health and reliability. We like to say “garbage in, garbage out.” It’s a true statement — but in today’s world, is that sufficient?
Businesses spend time, money, and resources buying solutions and building teams to manage all this infrastructure with the pipe(line) dream of one day being a well-oiled, data-driven machine — but data issues can occur at any stage of the pipeline, from ingestion to insights. And simple row counts, ad hoc scripts, and even standard data quality conventions at ingestion just won’t cut it.
#data-science #data-analysis #data-quality #towards-data-science #data #data analysis
The impulse to cut project costs is often strong, especially in the final delivery phase of data integration and data migration projects. At this late phase of the project, a common mistake is to delegate testing responsibilities to resources with limited business and data testing skills.
Data integrations are at the core of data warehousing, data migration, data synchronization, and data consolidation projects.
In the past, most data integration projects involved data stored in databases. Today, it’s essential for organizations to also integrate their database or structured data with data from documents, e-mails, log files, websites, social media, audio, and video files.
Using data warehousing as an example, Figure 1 illustrates the primary checkpoints (testing points) in an end-to-end data quality testing process. Shown are points at which data (as it’s extracted, transformed, aggregated, consolidated, etc.) should be verified – that is, extracting source data, transforming source data for loads into target databases, aggregating data for loads into data marts, and more.
Only after data owners and all other stakeholders confirm that data integration was successful can the whole process be considered complete and ready for production.
#big data #data integration #data governance #data validation #data accuracy #data warehouse testing #etl testing #data integrations