Enterprises today grapple with a goldmine of unstructured data—images, documents, videos, and audio—that holds untapped potential. Extracting actionable insights from this data has been a longstanding challenge, but the tide is turning with Generative AI (GenAI).
A Lakehouse is a new-age, open architecture that combines the best components of data lakes and data warehouses, enabling enterprises to power a wide range of analytics use cases – from business intelligence (BI) to artificial intelligence (AI).
Chetan Kalanki, Director of Cloud Engineering at Impetus, discusses the imperative for healthcare organizations to securely manage data, maintain application robustness, and comply with regulatory requirements to facilitate healthcare modernization.
Generative Adversarial Networks (GANs) are a powerful machine learning technique for generating synthetic data that is indistinguishable from real data. GANs have been used to generate synthetic images, text, audio, and video and have applications in a wide range of fields, including healthcare, finance, and security.
Data platform modernization is imperative for innovation and digital transformation across industries in today's data-driven world. However, as data volume, velocity, and complexity increase, traditional data warehousing solutions often fail to store, manage, and process data from multiple sources at scale to meet the demands of advanced analytics.
Many enterprises want to migrate from Confluent to Amazon MSK to scale storage capacity, save operational expenses, and enhance network security. Impetus Technologies, one of the ten launch partners for Amazon Managed Streaming for Apache Kafka (MSK) Delivery specialization, helped a global market leader in B2B digital sales migrate from Confluent to MSK.
Apache Kafka is a real-time event streaming platform that helps enterprises gain reliable insights for quick decision-making and improved customer experience. While it meets the enterprise streaming requirements, maintenance and management of Kafka is an overhead. To reduce these overheads, Amazon MSK (Managed service for Kafka) and Confluent Cloud are widely used by enterprises for event streaming with Apache Kafka.
The rapid scale of cloud adoption and digital transformation has spearheaded a massive change in the present technology landscape. Self-service tools, cloud-native applications, and data-driven technologies are redefining the traditional data stack. Within this landscape, the data mesh is fast emerging as a revolutionary paradigm for new-age analytics architecture.
The cloud empowers enterprises with on-demand scalability, flexibility, and cost benefits, enabling them to respond to fast-changing business requirements and fuel growth.
As enterprises struggle with poor data reliability, unscalable infrastructure, management complexities, excessive maintenance overheads, and unrealized value, they are looking to move their data and workloads to a cloud alternative.
The business impact of the COVID-19 pandemic continues to unfold worldwide for the financial services industry. The “new normal” has not only given rise to unprecedented operational challenges, but also provided fertile ground for hackers and threat actors to take advantage of increased vulnerabilities.
The unprecedented events of 2020 have profoundly impacted and accelerated technology trends across the world. COVID-19 brought digital transformation center stage, driving organizations to redefine their digital strategies across the enterprise at breakneck speed.
No matter what business you are in, cloud migration can be a daContainers and microservices are driving enterprise IT innovation and digital transformation across industries.
No matter what business you are in, cloud migration can be a daunting proposition. From choosing the right service provider to deciding a hosting strategy and selecting pricing models, there are many high-stake decisions involved.
By 2022, more than 75% of global organizations will be running containerized applications. – Gartner Inc.
Today, advances in artificial intelligence (AI) and machine learning (ML) have opened up significant application possibilities, from sensor-driven weather prediction to driverless cars to intelligent chatbots.
Snowflake is a popular cloud data warehouse choice for scalability, agility, cost-effectiveness, and a comprehensive range of data integration tools.
Data-driven decision-making is a key driver for enterprises in their digital transformation journey. Businesses are now switching to scalable, unified data storage repositories like enterprise data lakes, built on cloud storage options such as Amazon Simple Storage Service (S3), Google Cloud Storage, Azure Data Lake Storage (ADLS), and Azure Blob Storage.
Enterprises across industries are looking for a scalable, flexible, and adaptable data storage solution that supports a multitude of use cases, delivers real-time insights, and provides a unified view of all enterprise data.
Access control remains one of the biggest challenges of application security. Role-based access control (RBAC) and attribute-based access control (ABAC) are the most used access control models for system authorization, both of which have their own advantages.
While cloud adoption continues to accelerate, with 36% of enterprises spending more than $12 million per year on public clouds, businesses are looking for ways to optimize their cloud spend.
Technological advancements in the past decade have transformed the software development landscape significantly. Cloud services like Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) have led enterprises to sunset physical hardware and operating systems, respectively.
According to the Cisco Global Cloud Index, 94 percent of compute instances and workloads will be processed in the cloud data centers by 2021. Enterprises are eager to take advantage of the scalability, flexibility, efficiency, etc. that the cloud has to offer.
Enterprises are increasingly leveraging cloud-based data lakes to run large-scale analytics workloads and tap data-driven insights for better decision making. Cloud-based data lakes offer unmatched elasticity and scalability, enabling businesses to save costs and improve time-to-market.
Data estate modernization is typically a time-consuming and complex process, which requires extensive expertise and resources.
A comprehensive, end-to-end data and process lineage is quintessential for effectively planning the migration of legacy workloads to the Databricks Lakehouse.
Legacy data warehouses are choking under the weight of new unstructured and fast data sources, and enterprises are struggling to address challenges like secure data access, reliable backup storage, scalability, and increasing ownership costs.
The need for digital transformation is compelling enterprises to move from traditional data warehouses to the cloud. Gartner estimates that the worldwide public cloud services market will increase by over 17 percent to $206 billion in 2019.
Successful cloud migration involves understanding the responsibilities shared between an organization and its cloud service provider.
Enhanced application availability, improved performance, faster time-to-market, and easy scalability have made microservices a popular architectural choice for enterprises.
Making the move from EDW to the cloud can be daunting. A thorough understanding of requirements, possible scenarios, and processes is crucial to ensure a smooth transition. Organizations must also be equipped to deal with risks such as data loss, and even worse - failed implementation.
Business is booming in the data industry. Investments have grown exponentially in recent years and according to industry experts, the trend is expected to continue.
Legacy data warehouse transformation is complicated and risky. A successful migration requires a detailed evaluation on multiple parameters – including queries, tables, sub-queries, database views, users, applications, target query execution engines, and more.