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Westwing, a leading European eCommerce company, was facing challenges with its outdated technology stack and inefficient data architecture. The company recognized the importance of integrating data in a centralized location, but the manual work on their on-premise architecture was becoming increasingly time-consuming. To integrate with each different data source, every line of code had to be programmed with Python. This was slowing down the company's growth and preventing it from achieving a holistic view of the business. Westwing decided to move its architecture to the cloud, with Snowflake as the data warehouse, and outsource commodity services to focus on its strategic goal of scaling an eCommerce platform. However, with an ambitious cloud migration deadline looming, Westwing needed to find an ELT solution that could quickly and efficiently automate access to data.

WeWork, a global provider of flexible office spaces, faced the challenge of securely managing and leveraging its vast data resources to drive business decisions. As a publicly-traded company, WeWork had to meet stringent regulatory compliance requirements, ensuring data governance across disparate sources and silos. The company needed to track data movement and user access over time, demonstrating to auditors and regulators that customer information was safe from internal and external threats. The challenge was not only to pull in data and provide access but also to maintain a historical record of data ingestion, changes to the database, and access. The company also aimed to create a culture of data literacy and innovation, empowering business agility through the use of data.

World Fuel Services (WFS), a Fortune 150 company, faced significant challenges in managing and utilizing its data effectively. The company had grown through numerous acquisitions, each with its own client lists and data sources, making it difficult to gain a comprehensive view of customers across the entire organization. Additionally, the company's existing ETL pipelines pulled data in batches once a day into an on-premise Oracle database, which quickly became too large to run live queries effectively. The company also faced the challenge of managing data from dozens of ERP and billing information services across its subsidiaries, which was particularly critical during the global pandemic when the company needed to increase accounts receivable efforts to maintain revenue.

Yardzen, an online landscape design firm, was facing significant challenges in managing its data pipelines. The company's Data Engineering Lead, Andrea Kyrala, was tasked with integrating data from numerous SaaS tools and product databases into BigQuery, as well as establishing a flexible and secure data architecture. However, building custom pipelines to BigQuery in-house was a time-consuming process, often requiring weeks of work digging through API documentation. Moreover, the ETL pipelines were brittle and frequently required intensive maintenance. Analysts and marketers were manually exporting individual reports from each marketing platform to understand ad and marketing performance, a process that was not only painstaking and time-consuming but also made it difficult for leadership to gain a unified view of advertising spend and performance across platforms. Often, Andrea didn’t have the time for complex transformation and cleanup that would ultimately save the business analyst time in the backend.

YipitData, a trusted source of insights using alternative data, faced a significant challenge as it began to grow rapidly. The company's analytics activities were running on dozens of Amazon Redshift clusters, with each team within the company maintaining its own clusters. This arrangement became cumbersome, especially when YipitData needed to share common data sets across teams. The company's product teams analyze billions of data points each day to provide granular insights that drive the successful decision-making of hundreds of investment funds and highly innovative corporations. However, the existing system was slowing down analytics and making it difficult for the company to stay ahead of its market.

AES, a leading renewable-energy company, was faced with the challenge of accelerating their transition to renewable energy at scale. This business transformation necessitated a digital and AI transformation to better predict and optimize the energy output from renewable sources, predict failures, and optimize load distribution. The company had to deal with the complexities of wind-turbine predictive maintenance, energy bidding strategy for hydroelectric power plants, and smart meters. The maintenance of wind turbines, which have numerous moving parts subjected to harsh environments, was particularly costly and time-consuming. The company also needed to optimize its energy bidding strategy to maximize revenue from its hydroelectric power plants. Additionally, the company had to manage over one million smart meters, which sometimes had maintenance issues or were subject to misuse.

Tantalus, a provider of Smart Grid communications technology, was facing a significant challenge in managing and reporting data. Scott McKenzie, the VP of Load Management Operations, was responsible for tracking, analyzing, and reporting on data from various sources nested in Jira. The data focused on product shipments, returns, and support tickets. The process involved exporting Jira data onto an Excel spreadsheet, manipulating the data, creating custom graphs, and then incorporating these into reports for monthly senior management meetings. This process was not only repetitive but also time-consuming. Jira housed a large amount of granular data, and Scott only needed to look at a few major silos of data. Every month, he would download large spreadsheets with thousands of lines of data and begin analyzing. It would typically take days to build one monthly report. This manual and labor-intensive process was in dire need of an overhaul.

Atanasoft, a company that provides comprehensive product suites for large government agencies, banks, manufacturers, and retail companies to access big data stored in Teradata or other data warehouses, was facing a challenge. Their existing charting functions were limited in functionality and visual impact, with no support for dashboards. There was no easy way for customers to quickly create and customize charts, graphs, or dashboards. Atanasoft has always been at the leading edge for data analysis and visualizations and has relied upon Infragistics for its libraries of reliable, high-performance components. However, they needed a solution that could provide more advanced technology and visualizations.

Social services organizations are accountable to various stakeholders including funders, licensing agencies, managers, and program leadership. These stakeholders require regular reporting to measure impact, ensure professional standards, track progress, and evaluate staff compliance. However, many of these organizations lack dedicated data staff, making reporting an expensive and time-consuming task. Often, organizations rely on paper notes and forms for local or state reporting requirements, which then need to be transferred into a computer for generating reports. Casebook, a case management SaaS platform for the human services field, was designed with an intuitive user interface to simplify this process for its non-technical customers. However, they needed a reporting and visualization capability that integrated seamlessly into their software and offered the same intuitive experience.

Sensato, a top-500 cybersecurity innovator, was facing a significant challenge in visualizing and making sense of the massive amounts of data captured during a cyber attack. The company's integrated cybersecurity platform, Sensato-Nightingale, combines detection, monitoring, incident response, deception technology, forensics, countermeasures, and more. Once an attacker is detected, Nightingale can automatically launch its Sentinel technology to monitor, track, and report on the attacker’s activities across an organization’s network and enterprise. However, the sheer volume of data generated posed a constant challenge. Ongoing analysis was critical so that strategic security experts could develop more sophisticated protection strategies. The company needed a solution that could simplify analytics and visualizations, making them easy to use and deploy across multiple platforms, and speed up time to market.

Capital Consulting Group, led by Claudia 'Ms. Copywriter', was in need of a design software that could help her create graphics that fit her vision for her marketing and for her clients. The challenge was to find a tool that was versatile, intuitive, and cost-effective. The existing tools they had tried, such as Adobe and Canva, were either too complex or lacked versatility. The agency needed a solution that could help them create custom graphics for social media and printable banners for events in a more efficient and productive manner. The goal was to reduce the time spent on repetitive tasks and increase the productivity of the design team.

SmartBox Dental, a marketing agency specializing in dental practices, faced a significant challenge in making dental content engaging for their clients' audiences. Dental information, while crucial, is often perceived as dull and uninteresting, leading to a lack of engagement and retention. The content team at SmartBox Dental, led by Rebekah Carroll, was tasked with finding an innovative way to present this essential information in a manner that would capture and hold the audience's attention. The team, consisting of six members including Rebekah, four writers, and an editor, needed a tool that would allow them to create visually appealing content despite their lack of formal design training.

Bitminter, a pioneer in bitcoin mining pools, was facing a significant challenge in maintaining its network stability and uptime due to frequent DDoS attacks and extortion threats. Since its inception in 2011, Bitminter had attracted over 300,000 users and maintained 10% of bitcoin mining power at its peak. However, the company was a frequent target of DDoS attacks due to its high dependence on the internet. While these attacks rarely brought the company down for long periods, Bitminter was aware that any downtime could lead to users switching to other services. The company had tried setting up secondary and tertiary mining pools as a stopgap solution, but the attacks continued. Furthermore, the data center hosting Bitminter would null-route the company during attacks, effectively taking the service offline. This situation led to users leaving every time the mining pool was inaccessible.

Hactl, a leading air cargo terminal operator at Hong Kong International Airport (HKIA), handles an annual capacity of 3.5 million tonnes of air cargo. The company's COSAC-Plus air cargo management system is crucial for real-time tracking, documentation processing, and customs clearance. However, the effectiveness of COSAC-Plus depends on the uninterrupted operation of Hactl’s IT infrastructure, which requires a rapid, accurate, and uninterrupted flow of data 24/7. Given the evolving nature of cyber threats, Hactl recognized the need for proactive measures to bolster the resilience and availability of their IT infrastructure. The company sought a comprehensive solution that could protect its entire IT infrastructure from DDoS attacks.

Linkful Software, a leading IT solutions provider, faced a significant challenge in June 2013 when it was hit by a series of complex Zero Day attacks. These attacks, which targeted previously unknown vulnerabilities in the system, bypassed detection from mitigation services due to their sophisticated nature. The company was subjected to a combination of zombie, bandwidth, and DNS (Domain Name System) attacks over several days, causing extensive damage from service outages to infrastructure damage. Zombie attacks led to a 130-fold increase in connections, preventing legitimate users from accessing services. Bandwidth attacks flooded ICMP, UDP, and TCP SYN servers with up to 10 Gbps. These damages rendered services unavailable to users, threatening the company’s business operations and reputation as a reliable IT service provider.

Secure Network Operation Center (SNOC), a leading player in Thailand’s security marketplace, was facing a significant challenge. Its customers were increasingly falling victim to sophisticated DDoS attacks, leading to a growing demand for better protection. One customer, a fast-growing movie ticketing company, was particularly affected. The company's online ticketing site and mobile apps, hosted in SNOC’s datacenter, were frequently interrupted by severe attacks, threatening its growth and market leadership. The complexity of the threat, including volumetric, application, and mixed DDoS attacks, exceeded the capacity of traditional appliance protection. Furthermore, the appliance protection was not compatible with the company’s mobile apps, was not extensible enough to accommodate growth, required labor-intensive, 24/7 management, and incurred costly licensing fees for upgrades.

So-net Entertainment Taiwan, a subsidiary of Sony Network Communications Inc., faced a significant challenge in protecting its gaming hosting platforms from frequent, massive DDoS threats. As the host of a collection of popular mobile games, the company needed to ensure seamless protection against these threats to maintain the quality of service for its customers, many of whom are famous game developers. Prior to partnering with Nexusguard, So-net had been using a hardware-based DDoS mitigation vendor. However, the company was frustrated with the lack of timely response and technical support from the vendor. Despite being introduced to a system integrator for technical support, So-net was still unable to access the expertise required to handle DDoS attacks on its own, prompting the need for a change.

Boa Vista Serviços, a leading analytical-intelligence and credit-bureau player in Brazil, was facing challenges with its previous technology infrastructure. The company's physical servers located in three data centers were hindering its speed and ability to innovate. Limited scalability of resources and processes meant that they could not train many analytical models simultaneously or implement modern machine-learning techniques. One of its products was on the verge of halting sales because the company's processing capacity had reached its peak, directly impacting business expansion. The company needed to embrace digital transformation by migrating all its information from physical servers to the cloud. They also needed to switch from a traditional organizational model to a squad-based model to focus more efficiently on various technology and business aspects and challenges.

boodmo, India’s largest online marketplace for automotive components, faced a significant challenge in its early days. When the company first launched in 2015, there was a scarcity of customer data available. To make a lasting impact, boodmo had to gather and analyze most of this information itself. The company was using Google Analytics and Google Ads to gather data about its web activity and advertising, but it also needed to include more detailed information about its orders. Furthermore, boodmo wanted to build on its relationship with its customers and expand further. To do so effectively, the company had to carry out a comprehensive analysis of its customers as well as identify profitable marketing channels. However, pulling together different strands of data from multiple sources was not straightforward.

ACME Truck Line, a leading transportation company in the United States, was grappling with a labor-intensive and error-prone paper-based invoicing process. The company had to manually enter all invoicing data twice, once in its own accounting system and again in its customer’s invoice system. This double-entry practice not only increased the risk of errors and delays but also complicated the customer onboarding process. The challenge was further compounded by the fact that ACME's customers all had different invoice processing systems. The company was therefore faced with the daunting task of implementing an electronic invoicing solution that could seamlessly connect with all its customers' diverse systems.

Bloomberg Industry Group was faced with the challenge of integrating its Advantage Fixed Assets tax and accounting software into existing customer application and data infrastructures. The constant changes to tax laws and regulations often required the finance team to monitor and manage corporate data and assets in new and unexpected ways. This was problematic as the financial software and the corporate data and assets were not well and flexibly integrated. The changes often required finance professionals to gather data they had not had to track and monitor before, which may not even reside in their primary ERP systems. Tax changes could even be retroactive, requiring finance professionals to interact in new ways with systems and data repositories configured to support compliance with rules that no longer apply.

Bucap, a leading document archiving and management services provider, was faced with the challenge of managing and archiving massive amounts of data for public, private, and governmental organizations throughout Italy. The company needed a flexible, scalable database that could enable quick and easy customization of applications in response to evolving business requirements. The data may not be touched for decades, but some of it may be required at a moment’s notice. Bucap’s customers needed assurance that their data was secure and that they could access it without delay if and when necessary. In 1995, Bucap realized that meeting the company’s growth goals would depend on smart investments in IT. They needed to build an archiving solution that would be scalable, reliable, highly available, and easy to maintain to support all the business processes of storage, indexing, retrieving, and displaying documents at the service levels they wanted to offer their customers.

EidosMedia, a developer of digital publishing solutions, was faced with the challenge of creating a new digital editorial platform. The developers recognized that modern news consists of complex and relatively unstructured data, requiring a solution beyond a conventional relational database. The platform needed to manage and deliver content to a variety of emerging endpoints. The complexity and inconsistent structures of the content, which could range from text, graphics, audio, video, and more, posed serious challenges. The platform also needed to cater to an ever-widening range of devices, from televisions to smartphones, tablets, and more.

Expandium, a provider of network monitoring and management systems, was faced with the challenge of finding a robust analytics solution. Their mobile telco customers required near-real-time insights into the events impacting their large and complex networks, specifically those that could potentially affect their Service Level Agreements (SLAs). The need was for a solution that could be integrated into the intelligent systems Expandium was building, to provide deep visibility into network events, down to individual calls. The solution had to be capable of handling the massive volumes of data generated by these networks and deliver insights quickly enough for proactive issue resolution.

The advent of the Internet of Things (IoT) has revolutionized the manufacturing industry. The replacement of expensive PLC-based devices with cost-effective IoT-enabled devices has become a norm. Global Shop Solutions, a provider of a popular ERP solution for over 40 years, faced the challenge of meeting customer demands for IoT-enabled options. They needed a lightweight, embedded database solution that could interact efficiently with the Actian Zen database, the core of its ERP solution. The challenge was to find a serverless database that could be embedded within these inexpensive devices to store and process information. Using a tool like SQLite could meet the need for a small-footprint serverless database capable of collecting data, but sharing the data with any server-based database would require a middleware layer to transform the SQLite data into a format that another database could ingest.

A large global bank was faced with a significant challenge when support for its analytics solution, Netezza Twinfin, ended. The bank needed a new analytics solution that would not only replace Netezza but also deliver deeper insights faster, scale with the bank's growth, and keep costs low while delivering high performance. The bank required a solution that would support a single data repository for all positions across all asset classes, facilitating ad hoc analysis of positions and their sensitivity to market factors. The bank also wanted a solution that could ingest, analyze, and act on massive volumes of both structured and unstructured data in short order. The bank had long desired deeper and more timely insights into its risk exposure, as Netezza provided risk and opportunity value analyses only once per day via batch data dump. The bank aimed for a solution that could deliver insights in sub-minute intervals throughout the day. Performance, scalability, reliability, and cost were also critical factors in the bank's decision.

A mid-size enterprise software vendor, based in California, was struggling to optimize its sales and marketing operations in response to market disruptions caused by the Covid-19 pandemic. Despite major investments in solutions from Salesforce, Marketo, and other vendors, the company was unable to gain a comprehensive 360-degree view of its customers. The individual apps feeding into Salesforce CRM were providing only fragments of the total customer picture, and the Salesforce CRM solution itself was unable to present these fragmented pictures as quickly as the company’s fast-moving go-to-market initiatives demanded. The full, 360-degree customer view, accessible in real time, continued to elude the company. The advent of the Covid-19 pandemic had disrupted the dynamics of commerce like nothing in recent history. Uncertain economic conditions threatened to affect both the company and its customers, and the company needed to be able to respond more quickly and appropriately to customer needs and opportunities.

Netwrix, a company that provides information security and governance solutions, was facing growth and scalability challenges. The company needed to integrate its Salesforce and Oracle NetSuite solutions to support its transition from a small to a mid-sized company. The volume of inbound data was increasing, and there was a demand to process this data accurately and consistently across multiple systems. The old method of manually cutting, pasting, and reconciling data between systems was no longer efficient or effective. Furthermore, data integrity and security considerations required Netwrix to facilitate this integration in a way that protected and ensured the auditability of processes.

The Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU) at the University of Oxford was struggling with an aging legacy database infrastructure that could not handle the increasing volume of data nor run analytics against thousands of fields in multiple tables in a timely manner. The researchers at CTSU were dealing with vast amounts of information related to the causes and treatments of chronic illnesses such as cancer, heart disease, and stroke. They were seeking greater insights into links between causes and diseases as well as prevention and treatment options. However, extracting insights from the ever-increasing volume of data was becoming more and more difficult. Their ad hoc analytical queries could take multiple days to return a response. The researchers needed a faster, more cost-effective way to identify, manipulate, and extract the exact data they needed from the large pool of data they had accumulated, and gain insights from that data far faster than the legacy infrastructure could accommodate.

Pinnergy, a leading diversified energy services company, was facing significant challenges with its manual, double-entry invoicing process. The process was not only tedious but also time-consuming, taking between 30 and 90 days to get an invoice entered into a client’s Electronic Data Interchange (EDI) system. The company's customers, which included a wide range of large and small oil and gas industry firms, did not rely on any common back-office system, making electronic invoicing a complex task. As Pinnergy’s business grew, the order management became even more complex with more customers to invoice and higher volumes of transactions to manage. The invoicing process became a bottleneck, resulting in high transaction costs and accounts receivable aging. Pinnergy officials knew that an electronic invoicing solution would dramatically streamline this process, but the challenge was to find a solution that could integrate with the diverse EDI systems of their customers.