In this paper we discuss the cloud data journey and its associated issues, focusing on the use case of deploying new cloud analytics requiring existing on-premises data. We look at how organizations should use modern data platforms such as Datameer Spotlight and Spectrum to help avoid these issues. We describe best practices for deploying and using analytics data in the cloud, and explore how the right third-party tool can help implement data analytics securely and at scale in the cloud. Finally, we provide generalized advice for migrating from on-premises to the cloud and provide a rationale for using a third-party toolset to enable and accelerate successful migrations.

Findings include:

• “Big Three” cloud providers offer only basic data management capabilities to deliver hybrid cloud analytics
• Both data virtualization and data pipelines have emerged as significant features for optimized cloud analytics
• Third-party platforms such as Datameer offer more extensive capabilities
• We can learn practices from the field that promote successful data management for analytics in the cloud

In conclusion, we would advise organizations looking to bridge large quantities of data with cloud analytics to look beyond the default functions, and to plan carefully.

The post Analytics in the Cloud: Minimize Pain, Maximize Success appeared first on Gigaom.

Enterprises need a way to consolidate disparate data sources logically without forcing them to be physically moved or transformed. They need help too in managing, manipulating, and presenting a unified view of data to the downstream consumer. Years of progress have yielded a solution, in the form of data virtualization platforms.

This GigaOm Radar report analyzes the top data virtualization platforms in the market, weighs the key criteria and evaluation metrics used to assess these solutions, and identifies important technologies to consider for the future. This report gives organizations an overview of the leading data virtualization platforms in the market today, and recognizes platforms that excel in particular categories. It is intended to appeal both to organizations looking to extend investments in existing platforms as well as to those yet to dip their toe in the data virtualization waters.

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This global restructuring of work has produced a stratospheric rise in the deployment and use of video conferencing and collaboration platforms at organizations of every type and size. But that transition has been anything but orderly, as IT organizations moving to adopt enterprise-focused video conferencing platforms ran afoul of end users already fiercely loyal to easy-to-use, consumer-friendly video conferencing services–most notably, Zoom. The result has been a struggle that has pitted enterprises focused on priorities like security, privacy, and integrations against workers focused on familiarity and ease of use.

It is no surprise that the instant ubiquity of video conferencing has kicked off intense activity in the sector. We have seen significant investment in previously moribund platforms even as new offerings have sprung up. At the same time, the distinction between enterprise and consumer video conferencing platforms is blurring, as vendors in each camp adopt features that appeal to the other.

Video conferencing platforms have already assumed a position alongside email as a core channel for communication and collaboration. Their widespread use and increasing integration into both personal and professional life means we can expect video conferencing platforms to evolve and extend, adding capabilities ranging from team collaboration features to immersive, virtual environments that place video of participants into a shared digital space.

In this report, we explore the fast-changing arena of video conferencing platforms and gauge their capabilities. We assess the most important features of these platforms, breaking them into base-level table stakes, differentiating key criteria, and forthcoming emerging technologies. We further identify and explore a series of top-line characteristics for these products, in order to help IT decision makers better judge which platforms are best suited to their needs.

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Data virtualization platforms address this situation, and help manage, manipulate, query, and present a unified view to the downstream consumer. Data virtualization solutions enhance an organization’s ability to consolidate disparate data sources logically, without forcing them to be physically moved or transformed. Years of experience have helped the platforms mature, and have clarified the core set of principles this market revolves around.

This GigaOm Key Criteria report explores the data virtualization space, describes key criteria and evaluation metrics for selecting a solution platform, and identifies key technologies for the future. The report also provides the groundwork to help organizations make informed decisions about data virtualization platforms, with a detailed examination of available solutions provided in the GigaOm Radar report that follows.

Key Findings

• Data virtualization is a key component of an organization’s data management culture
• The term is still somewhat nebulously defined, but some concepts are almost universally applicable
• At its core, the technology really is all about unifying data access to a vast array of disparate data sources; it is how, and how well, they do so that differentiates most of the technology solutions
• Many architectural approaches exist around platform implementation, making selection of an appropriate solution more difficult than in other technology areas
• A virtual, semantic, business-friendly data layer is a key component of the most successful platforms
• Ease of use, accelerated performance, unified data management, governance, data discoverability, and metadata management capabilities are all very important to organizations
• Collaboration around virtualized data assets is an emerging trend
• There is room for multiple winners in the marketplace
• Major data virtualization platform features include:
  • Virtual data access and logical data layer
  • Data federation
  • Unified data management
  • Data abstraction
  • Data collaboration
  • Data transformation
  • No-copy, in-place data access
  • Data connectivity and consumption
  • Data integration
  • Data replication and copying
  • Data governance and security
  • Unified data caching

The post Key Criteria for Evaluating Data Virtualization appeared first on Gigaom.

This report will review the major cloud data pipeline services from the big three providers and look at each one’s commonalities and unique capabilities. We will examine: Azure Data Factory, AWS Glue, AWS Data Pipeline, GCP Dataflow and GCP Cloud Data Fusion. The report will also explore and discuss how far the services’ out-of-the-box functionality will take you, and how to extend that functionality where necessary, with data source connectivity as a specific axis of extensibility. We will also enumerate basic differences between cloud and on-premises data pipelines and discuss how extensibility frameworks can help in both cases.

If you have already selected a public cloud provider for your business, this report will help you better understand the data pipeline capabilities available on the platform. And if you are deciding which cloud to standardize on, this report will help you factor data pipeline capabilities into that decision process. If you are still maintaining on-premises data infrastructure, this report will provide relevant information on what to consider as you look to modernize. In all cases, you will gain perspective on how and when to go beyond the pipeline services’ standard capabilities, using compatible third-party technologies that may add significant value.

The post Cloud Data Pipeline Services: Foundational Platforms with Expandable Capabilities appeared first on Gigaom.

This GigaOm report details the key criteria and evaluation metrics needed to select a video conferencing platform. It gives organizations an overview of the salient video conferencing technologies, with a particular emphasis on how the new reality of a world in the midst of a pandemic will influence platform selection for both the enterprise and home.

Key Findings

• Video conferencing has been transformed from a relatively stable enterprise concern to one that combines consumer and enterprise interests.
• Growth in all platforms has been phenomenal, as the world has been forced into unprecedented work-from-home situations.
• A clear winner in the consumer space has emerged in Zoom, which has rocketed ahead of all other platforms in name recognition, user adoption, and cultural impact. Significant security and privacy concerns around both the company and its platform, however, have made a lot of organizations skeptical about using it.
• Traditional enterprise vendors have scrambled to catch up to Zoom’s popularity by investing heavily in consumer-focused concerns to boost ease of use and video conferencing capabilities.
• New video conferencing platforms from consumer giants have been developed from scratch during the pandemic, joining an already crowded field.
• Enterprise and consumer audiences tend to value different platform features. This wasn’t an issue pre-pandemic, but with work and personal lives merging in the current environment, organizations must evaluate the relative merits of use against security.
• Major video conferencing and team collaboration platform features include:
  • Ease of use
  • Security and compliance
  • Video and audio conferencing abilities, one-on-one or with multiple people
  • Team chatting
  • Collaboration capabilities
  • Screen sharing
  • Video recording
  • Remote control capabilities
  • Custom backgrounds and blurring
  • Desktop, mobile, and browser support
  • Automatic on-the-fly closed captioning
  • Breakout rooms
  • Whiteboarding
  • Hardware rooms and workspaces
  • Phone system support
  • Webinar support

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The new breed of Database as a Service (DBaaS) cloud platforms offer many advantages over traditional counterparts, but it would still be imprudent to break completely from the on-premises past and present. Diligent organizations will survey the market and take a holistic approach that deals with their data needs in totality.

This GigaOm Radar report details the key cloud database platforms in the market and identifies vendors and products that excel. This report will give you an overview of key cloud database platforms, helping you evaluate your existing investments and informing your new ones. Key findings include:

• Several new cloud-native platforms have been developed, both by traditional on-premises vendors as well as new entrants.
• Many organizations are already using the platforms extensively and usage is accelerating.
• Building on cloud-native architectures enables significant flexibility in deployment of the platforms across public and private clouds as well as on-premises.
• The platforms enable key scenarios around elasticity, performance, data distribution, automation, and data replication that are very difficult to get right in an on-premises environment. This is due to the underlying cloud architecture and is expected to lead to even more capabilities down the line.
• SQL continues to be the dominant querying language in most cases; however, extensive support for both multi-model databases and the NoSQL model is widely observed.
• We have grouped the platforms in this report into two distinct categories: relational model database platforms and NoSQL database platforms

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In this report, we explore these new platforms, which take advantage of the cloud’s inherent qualities—boosting elasticity, reducing cost of ownership, increasing ease of use, creating greater operational simplicity, improving flexibility, and more.

This new class of Database-as-a-Service (DBaaS) cloud platforms demonstrates multiple advantages over more traditional platforms, but most organizations would be unwise to break completely from the old techniques of on-premises and hybrid cloud. Successful organizations will take into account solutions in the market, the capabilities and needs of their internal teams, and their own strategic plans before making decisions on which platforms to adopt.

This GigaOm Key Criteria Report identifies key criteria and evaluation metrics for selecting a cloud database platform. It outlines how to identify the basic requirements of a system, what features serve to differentiate solutions within the market, and what new features now being developed and deployed that we predict will become relevant within the market. It also explores the top-line characteristics—known as evaluation metrics—that ultimately drive decision making.

Key Findings

This report produces a number of key findings. These include:

• Cloud database platforms are a relatively new development in the long story of database systems. They are, however, based solidly on the established theoretical work done over the past forty years in the areas of relational, object, document, and graph databases. These mature principles are supplemented by the distributed data, replication, elasticity, scaling, and operational characteristics that the cloud enables. Taken together, these concepts represent an important advancement in database systems principles.
• Building on cloud-native architectures enables significant flexibility in deployment of the platforms, across public and private clouds as well as on-premises.
• SQL continues to be the dominant querying language in most cases; however, extensive support for multi-model databases and the NoSQL model is widely observed.

The post Key Criteria for Evaluating Cloud Database Platforms appeared first on Gigaom.

For decades data warehouses have been the trusted technology for large-scale data storage and analytics in the enterprise. And that role has become only more vital, as traditional data warehouse vendors have modernized their products to provide advanced scaling capabilities, massive parallelism, enhanced ease of use, and reduced total cost of ownership. At the same time, vendors are evolving new features and advancing their architectures to leverage the native capabilities of the cloud.

Today, vendors are extending their products, moving from core data warehouse offerings to more integrated platforms with warehouse capabilities at their core. These include integrations with formerly discrete technologies such as data lakes, Hadoop and Spark, as well as AI operations, deep integration with data analytics and other BI tools, and easier integration with data engineering, data science and machine learning workflows. Built-in data governance, data quality and data preparation are also included.

Managing the full data cycle is now virtually impossible for an organization without a data warehouse platform. Evaluating the market’s most important vendors is therefore vital for any organization. This process needs to examine both technical and non-technical considerations, and should be approached holistically.

This GigaOm Radar report examines and evaluates the most important data warehouse platforms in the market today. It looks at each vendor’s approach, capabilities and, crucially, its ongoing development, and explores how each is poised to evolve over the next twelve months.

This report is designed to help you evaluate both the current and future position of solutions within the market. The aim is to help your organization make the best possible decision about the vendor it selects for its data warehouse.

Key findings:

• New platform development is focused on the cloud. Several cloud-native platforms have emerged, and more traditional vendors have modernized their platforms to transition from on-premises to the cloud.
• Vendors are investing heavily in hybrid cloud capabilities. Data sets spanning on-premises and the cloud can now be processed and analyzed, and disaster recovery capabilities have been enhanced.
• Most vendors are integrating data science, ML, and artificial intelligence (AI) capabilities into their base data warehousing architecture.
• Data warehouses are able to store and analyze data more quickly, allowing huge amounts of data to be explored more readily, and this trend is accelerating.
• SQL is the dominant querying language, though vendors do make their own additions or incorporate special variations.
• Integration with data lakes, query federation, and processing of remote data sets in-place are slowly becoming more prevalent, with several vendors implementing similar performance-enhancing features.

The post GigaOm Radar for Evaluating Data Warehouse Platforms appeared first on Gigaom.

Additional enhancements to the platforms have been implemented widely as we have moved from a core data warehouse offering to a more integrated platform with warehouse capabilities at its core. These include integrations with formerly separate technologies, such as data lakes, Spark and Hadoop, as well as autonomous operations, tight integration with business intelligence (BI) tools, integrations with data engineering, data science and machine learning (ML) workflows, and built-in data governance, data quality and data prep capabilities.

A data warehouse platform is now a critical component of an organization’s path to managing the full data lifecycle. It should be evaluated on both technical and non-technical capabilities holistically.

This GigaOm Key Criteria report identifies key criteria and evaluation metrics for selecting a platform. This report will give you an overview of the technologies that are critical to understand as you make informed decisions about the platforms in which you plan to invest.

Key Findings

• Data warehousing is a mature, well-understood technology category, widely-used in organizations of all sizes to enable massive data sets to be stored and analyzed.
• The cloud is where most new platform development is now happening. Several new cloud-native platforms have emerged, and traditional vendors have created new offerings to modernize their platforms or enable transitions from on-premises to the cloud. Hybrid cloud capabilities are also receiving investment, allowing data sets spanning on-premises and the cloud to be processed and analyzed, as well as enabling enhanced disaster recovery capabilities.
• Integration of data science, ML, and artificial intelligence (AI) capabilities with the base data warehousing architecture is being widely pursued by most vendors. Different approaches and levels of integration are currently being attempted, with further enhancements on the horizon.
• As scaling technologies mature, even more massive amounts of data are now able to be economically and quickly stored and analyzed.
• SQL continues to be the dominant querying language, oftentimes with vendor-specific additions or variations to the standard..
• Integration with data lakes, query federation, and processing of remote data sets in place, is becoming more prevalent, with several vendors implementing performance-enhancing features.

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