Notes about Cutting-Edge Technologies and Everything

As an industry, we’ve gotten exceptionally good at building large, complex software systems. We’re now starting to see the rise of massive, complex systems built around data – where the primary business value of the system comes from the analysis of data, rather than the software directly. We’re seeing quick-moving impacts of this trend across the industry, including the emergence of new roles, shifts in customer spending, and the emergence of new startups providing infrastructure and tooling around data. In fact, many of today’s fastest growing infrastructure startups build products to manage data. These systems enable data-driven decision making (analytic systems) and drive data-powered products, including with machine learning (operational systems). They range from the pipes that carry data, to storage solutions that house data, to SQL engines that analyze data, to dashboards that make data easy to understand – from data science and machine learning libraries, to automated data pipe...

Databricks SQL   already provides a first-class user experience for BI and SQL directly on the data lake, and today, we are excited to announce another step in making data and AI simple with Databricks Serverless SQL. This new capability for Databricks SQL provides instant compute to users for their BI and SQL workloads, with minimal management required and capacity optimizations that can lower overall cost by an average of 40%. This makes it even easier for organizations to expand adoption of the lakehouse for business analysts who are looking to access the rich, real-time datasets of the lakehouse with a simple and performant solution. Under the hood of this capability is an active server fleet, fully managed by Databricks, that can transfer compute capacity to user queries, typically in about 15 seconds. The best part? You only pay for Serverless SQL when users start running reports or queries. Organizations with business analysts who want to analyze data in the data lake with t...

  Snowflake data sharing and data marketplace can support modern data sharing techniques and eliminate the need for data movement. In Snowflake, there is no need to extract the data from the provider database and use some secure data transfer mechanism to share it with the consumers. Snowflake supports data sharing embedded into their SQL language so databases can be shared from within SQL commands. And on top of that, the data provider can update the data in real-time ensuring that all consumers will have a consistent, up-to-date view of their data sets. How Data Sharing Works Snowflake can share regular and external tables, and secure views and secure materialized views. Snowflake enables the sharing of databases through the concept of shares. Continue reading >>>

As companies embrace digital technologies to transform their operations and products, many are using best-of-breed software, open source tools, and software as a service (SaaS) platforms to rapidly and efficiently integrate new technologies. This often means that data required for reports, analytics, and machine learning (ML) reside on disparate systems and platforms. As such, IT initiatives in companies increasingly involve tools and frameworks for data fusion and integration. Examples include tools for building data pipelines, data quality and data integration solutions, customer data platform ( CDP ) ,   master data management , and   data markets . Collecting, unifying, preparing, and managing data from diverse sources and formats has become imperative in this era of rapid digital transformation. Organizations that invest in  foundational data technologies  are much more likely to build solid foundation applications, ranging from BI and analytics to machine learn...

In the past year or two, many companies have shared their data discovery platforms (the latest being Facebook’s Nemo). Based on this list, we now know of more than 10 implementations. I haven’t been paying much attention to these developments in data discovery and wanted to catch up. I was interested in: The questions these platforms help answer The features developed to answer these questions How they compare with each other What open source solutions are available By the end of this, we’ll learn about the key features that solve 80% of data discoverability problems. We’ll also see how the platforms compare on these features, and take a closer look at open source solutions available. Questions we ask in the data discovery process Before discussing platform features, let’s briefly go over some common questions in the data discovery process. Where can I find data about ____? If we don’t know the right terms, this is especially challenging. For user browsing behavior, do we search for “c...

Netflix has more than 195 million subscribers that generate petabytes of data everyday. Data scientists and engineers collect this data from our subscribers and videos, and implement data analytics models to discover customer behaviour with the goal of maximizing user joy. Usually Data scientists and engineers write Extract-Transform-Load (ETL) jobs and pipelines using big data compute technologies, like Spark or Presto, to process this data and periodically compute key information for a member or a video. The processed data is typically stored as data warehouse tables in AWS S3. Iceberg is widely adopted in Netflix as a data warehouse table format that addresses many of the usability and performance problems with Hive tables. At Netflix, we also heavily embrace a microservice architecture that emphasizes separation of concerns. Many of these services often have the requirement to do a fast lookup for this fine-grained data which is generated periodically. For example, in order to enha...

Millions of Uber trips take place each day across nearly 80 countries, generating information on traffic, preferred routes, estimated times of arrival/delivery, drop-off locations, and more that enables us to facilitate better experiences for users. To make our data exploration and analysis more streamlined and efficient, we built Uber’s data science workbench (DSW), an all-in-one toolbox for interactive analytics and machine learning that leverages aggregate data. DSW centralizes everything a data scientist needs to perform data exploration, data preparation, ad-hoc analyses, model exploration, workflow scheduling, dashboarding, and collaboration in a single-pane, web-based graphical user interface (GUI). Leveraged by data science, engineering, and operations teams across the company, DSW has quickly scaled to become Uber’s go-to data analytics solution. Current DSW use cases include pricing, safety, fraud detection, and navigation, among other foundational elements of the trip experi...

This month’s release delivers very useful features like Apache Arrow Flight with Python, full support for CDP 7.1, security enhancements for Oracle connections, a new support bundle and much more. This blog post highlights the following updates: Arrow Flight clients Query support bundle Kerberos support for Dremio-Oracle connections User/job metrics available in the UI Continue reading >>>

Solutions Review’s Data Integration Tools Vendors to Watch is an annual listing of solution providers we believe are worth monitoring. Companies are commonly included if they demonstrate a product roadmap aligning with our meta-analysis of the marketplace. Other criteria include recent and significant funding, talent acquisition, a disruptive or innovative new technology or product, or inclusion in a major analyst publication. Data integration tools vendors are increasingly being disrupted by cloud connectivity, self-service, and the encroachment of data management functionality. As data volumes grow, we expect to see a continued push by providers in this space to adopt core capabilities of horizontal technology sectors. Organizations are keen on adopting these changes as well, and continue to allocate resources toward the providers that can not only connect data lakes and Hadoop to their analytic frameworks, but cleanse, prepare, and govern data. The next generation of tools will offe...

Our aspiration to augment and improve every aspect of business and life with data, demands a paradigm shift in how we manage data at scale. While the technology advances of the past decade have addressed the scale of volume of data and data processing compute, they have failed to address scale in other dimensions: changes in the data landscape, proliferation of sources of data, diversity of data use cases and users, and speed of response to change. Data mesh addresses these dimensions, founded in four principles: domain-oriented decentralized data ownership and architecture, data as a product, self-serve data infrastructure as a platform, and federated computational governance. Each principle drives a new logical view of the technical architecture and organizational structure. The original writeup, How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh - which I encourage you to read before joining me back here - empathized with today’s pain points of architectural and or...

Have we entered the Golden Age of Data? Modern enterprises are collecting, producing, and processing more data than ever before. According to a February 2020 IDG survey of data professionals, average corporate data volumes are increasing by 63% per month. 10% of respondents even reported that their data volumes double every month. Large companies are investing heavily to transform themselves into data-driven organizations that can quickly adapt to the fast pace of a modern economy. They gather huge amounts of data from customers and generate reams of data from transactions. They continuously process data in an attempt to personalize customer experiences, optimize business processes, and drive strategic decisions. The Real Challenge with Data In theory, breakthrough open-source technologies, such as Spark, Kafka, and Druid are supposed to help just about any organization benefit from massive amounts of customer and operational data just like they benefit Facebook, Apple, Google, Microso...

Large-scale companies serve millions or even billions of people who depend on the services these companies provide for their everyday needs. To keep these services running and delivering meaningful experiences, the teams behind them need to find the most relevant and accurate information quickly so that they can make informed decisions and take action. Finding the right information can be hard for several reasons. The problem might be discovery — the relevant table might have an obscure or nondescript name, or different teams might have constructed overlapping data sets. Or, the problem could be one of confidence — the dashboard someone is looking at might have been superseded by another source six months ago.  Many companies, such as Airbnb, Lyft, Netflix, and Uber, have built their own custom solutions for this challenge. For us, it was important to make the data discovery process simple and fast. Funneling everything through data experts to locate the necessary data each time we...

Apache Pulsar's unique features such as tiered storage, stateless brokers, geo-aware replication, and multi-tenancy may be a reason to choose it over Apache Kafka. Today, many data architects, engineers, dev-ops, and business leaders are struggling to understand the pros and cons of Apache Pulsar and Apache Kafka. As someone who has worked with Kafka in the past, I wanted to compare these two technologies.  If you are looking for insights on when to use Pulsar, here are 10 advantages of the technology that might be the deciding factors for you. Continue reading >>>

AIOps is an emerging technology and addresses something I’m a big fan of – improving IT Operations.  So I asked fellow Gartner analyst Colin Fletcher for a guest blog on the topic… Roughly three years ago, it was looking like we were going to see many enterprise IT operations leaders put themselves in the precarious role of “ the cobbler’s children ” by forgoing investment in Artificial Intelligence (AI) to help them do their work better, faster, and cheaper. We were hearing from many IT ops leaders building incredibly sophisticated Big Data and Advanced Analytics systems for business stakeholders, but were themselves using rudimentary, reactive red/yellow/green lights and manual steps to help run the infrastructure required to keep those same systems up and running. Further, we’re all now familiar in our personal lives with dynamic recommendations from online retailers, search providers, virtual personal assistants, and entertainment services, Talk about a paradox! Now I...

Today we are excited to announce the release of Dremio 4.8! This month’s release delivers multiple features such as external query, a new authorization service API, AWS Edition enhancements and more. This blog post highlights the following updates: External query Default reflections Runtime filtering GA Documented JMX metrics and provided sample exporters Ability to customize projects in Dremio AWS Edition Support for Dremio AWS Edition deployments without public IP addresses Read full article >>>

Once an outsider category, cloud computing now powers every industry. Look no further than this year’s Forbes Cloud 100 list, the annual ranking of the world’s top private cloud companies, where this year's standouts are keeping businesses surviving—and thriving—from real estate to retail, data to design. Produced for the fifth consecutive year in partnership with Bessemer Venture Partners and Salesforce Ventures, the Cloud 100 recognizes standouts in tech’s hottest category from small startups to private-equity-backed giants, from Silicon Valley to Australia and Hong Kong. The companies on the list are selected for their growth, sales, valuation and culture, as well as a reputation score derived in consultation with 43 CEO judges and executives from their public-cloud-company peers. This year’s new No. 1 has set a record for shortest time running atop the list. Database leader Snowflake takes the top slot, up from No. 2 last year and just hours before graduating from the list by g...

Finding the right data quickly is critical for any company that relies on big data insights to make data-driven decisions. Not only does this impact the productivity of data users (including analysts, machine learning developers, data scientists, and data engineers), but it also has a direct impact on end products that rely on a quality machine learning (ML) pipeline. Additionally, the trend towards adopting or building ML platforms naturally begs the question: what is your method for internal discovery of ML features, models, metrics, datasets, etc.? In this blog post, we will share the journey of open sourcing DataHub, our metadata search and discovery platform, starting with the project’s early days as WhereHows. LinkedIn maintains an in-house version of DataHub separate from the open source version. We will start by explaining why we need two separate development environments, followed by a discussion on the early approaches for open sourcing WhereHows, and a comparison of our inte...

IEEE DataPort™ is a valuable and easily accessible data platform that enables users to store, search, access and manage data.  The data platform is designed to accept all formats and sizes of datasets (up to 2TB), and it provides both downloading capabilities and access to datasets in the Cloud.  IEEE DataPort™ is a universally accessible web-based portal that serves four primary purposes:   Enable individuals and institutions to indefinitely store and make datasets easily accessible to a broad set of researchers, engineers and industry;   Enable researchers, engineers and industry to gain access to datasets that can be analyzed to advance technology; Facilitate data analysis by enabling access to data in the AWS Cloud and by enabling the downloading of datasets Supports reproducible research. IEEE DataPort™ is an online data platform created and supported by IEEE and supports IEEE’s overall mission of Advancing Technology for Humanity. ...

Modern world data and its usage has drastically changed when compared to a decade ago. There is a gap caused by the traditional ETL processes when processing modern data. The following are some of the main reasons for this:  Modern data processes often include real-time streaming data, and organizations need real-time insights into processes.  The systems need to perform ETL on data streams without using batch processing, and they should handle high data rates by scaling the system. Some single-server databases are now replaced by distributed data platforms ( e.g., Cassandra, MongoDB, Elasticsearch, SAAS apps ), message brokers( e.g., Kafka, ActiveMQ, etc. ) and several other types of endpoints. The system should have the capability to plugin additional sources or sinks to connect on the go in a manageable way. Repeated data processing due to ad hoc architecture has to be eliminated. Change data capture technologies used with traditional ETL has to be integ...

Expert data scientists and other professionals working in data science roles require capabilities to source data, build models and operationalize machine learning insights. Significant vendor growth, product development and myriad competing visions reflect a healthy market that is maturing rapidly. This Magic Quadrant evaluates vendors of data science and machine learning (DSML) platforms. Gartner defines a DSML platform as a core product and supporting portfolio of coherently integrated products, components, libraries and frameworks (including proprietary, partner and open source). Its primary users are data science professionals. These include expert data scientists, citizen data scientists, data engineers and machine learning (ML) engineers/specialists. Coherent integration means that the core product and supporting portfolio provide a consistent “look and feel” and create a user experience where all components are reasonably interoperable in support of an analytics pipel...