With Fivetran, Snowflake and dbt, Autodesk Construction Services builds a uniform data architecture for its many acquisitions.

Let's explore why data automation is the bridge between enterprise analysts and IT/Engineering departments.

In a global economy, real-time data analysis is closely related to business success. Without data-driven insights, organizations find it challenging to remain competitive, improve company performance, and deliver strong user experiences, regardless of their industry. To match the pace of business, companies require transparent, data-driven relationships.

Medical devices have become increasingly complex as technology evolves, and the sheer number of these devices now being worn or implanted has grown exponentially over the past few years. There are currently over 500,000 different types of smart, connected medical devices in use that have the ability to collect, share, or store private patient data and protected health information (PHI)(1).

Coronavirus has impacted the travel industry, but as it adapts, there is one factor airlines have always worked hard to minimize: delayed flights. Arriving late or missing a connection can severely impact the customer experience, which is why airlines work hard to maintain high rates of on-time performance (OTP). To that end, pilots may have to use extra fuel to make up for a delayed departure or to reach a destination early, even if it means circling the airport before landing.

Advanced marketing analytics can improve campaign relevance, increase customer lifetime value, accelerate insights, reduce acquisition costs, and drive ROI. But moving to advanced analytics requires a thoughtful investment in the right infrastructure for storing, tracking, and analyzing customer data, which can be daunting to companies that only have basic analytics capabilities.

Use our dbt package for HubSpot to build your sales and marketing dashboards.

Learn why ELT is better than ETL and how you can get started with it.

SQL has long been the universal language for working with data. In fact it’s more relevant today than it was 40 years ago. Many data technologies were born without it and inevitably ended up adopting it later on. Apache Kafka is one of these data technologies. At Lenses.io, we were the first in the market to develop a SQL layer for Kafka (yes, before KSQL) and integrate it in a few different areas of our product for different workloads.

If you’re an engineer exploring a streaming platform like Kafka, chances are you’ve spent some time trying to work out what’s going on with the data in there. But if you’re introducing Kafka to a team of data scientists or developers unfamiliar with its idiosyncrasies, you might have spent days, weeks, months trying to tack on self-service capabilities. We’ve been there.

From data stagnating in warehouses to a growing number of real-time applications, in this article we explain why we need a new class of Data Catalogs: this time for real-time data. The 2010s brought us organizations “doing big data”. Teams were encouraged to dump it into a data lake and leave it for others to harvest. But data lakes soon became data swamps.

The next generation of 5G networks are unlocking a mind-bending array of new use cases. Blistering speed, super low latency, and access to more powerful mobile hardware bring VR, AR and ultra high-definition experiences into sharp focus for the near future. But there’s a bigger shift being driven by 5G, and it’s not actually about speed at all. It’s about re-thinking the modern telco business model.

Navistar is a leading global manufacturer of commercial trucks. With a fleet of 350,000 vehicles, unscheduled maintenance and vehicle breakdowns created ongoing disruption to their business. Navistar required a diagnostics platform that would help them predict when a vehicle needed maintenance to minimize downtime.

Strong investments in an organization’s data pipeline result in greater business outcomes. Few would dispute this claim, reflected in the massive growth in the big data and analytics market which continues to fuel many organizations’ ambition to become data-driven.

“Quality is “value to some person” (quoting Jerry Weinberg) and as such is highly subjective. Because of that, there is rarely a set of metrics that can be used as a recipe for most cases; which in turn makes indicators of readiness to deploy, data to be collected, etc.

Apache Hive supports transactional tables which provide ACID guarantees. There has been a significant amount of work that has gone into hive to make these transactional tables highly performant. Apache Spark provides some capabilities to access hive external tables but it cannot access hive managed tables. To access hive managed tables from spark Hive Warehouse Connector needs to be used.

As a result of the COVID-19 pandemic, organizations around the world have had to transform overnight. Businesses that had been delaying digital transformation, or that hadn’t been thinking about it at all, have suddenly realized that moving their data analytics to the cloud is the key to coping with and surviving the COVID-19 disruption. The next phase is about rebounding and thriving in a post-COVID-19 world.

Being data-driven helps businesses to cut costs and produce higher returns on investments, increasing their financial viability in the fight for a piece of the market pie. But *becoming* data-driven is a more labor-intensive process. In the same way that companies must align themselves around business objectives, data professionals must align their data around data models. In other words: if you want to run a successful data-driven operation, you need to model your data first.

Over the last 25 years, I have an unparalleled front seat to the digital transformation that is now accelerating in the connected manufacturing and automotive industry. Not many people have had the opportunity to witness the transformation and be as active in this area as I have; I consider myself lucky.

Most of us know the story of “The Tortoise and the Hare.” It is one of Aesop’s classic fables in which a speedy, overconfident hare becomes complacent and realizes, all too late, that the tortoise, although outmatched, has managed to beat him in a race. It teaches us lessons about overconfidence and perseverance and has caused phrases like “slow and steady wins the race” to creep into our everyday language.

“You cannot be the same, think the same and act the same if you hope to be successful in a world that does not remain the same.” This sentence by John C. Maxwell is so relevant to rapidly changing cloud hosting technology. Businesses understand the added value and are looking at cloud technologies to handle both operational and analytical workloads.

This blog post is a first of a series on how to leverage PyTorch’s ecosystem tools to easily jumpstart your ML / DL project. The first part of this blog describes common problems appearing when developing ML / DL solutions, and the second describes a simple image classification example demonstrating how to use Allegro Trains and PyTorch to address those problems.

Survival analysis is one of the most developed fields of statistical modeling, with many real-world applications. In the realm of mobile apps and games, retention is one of the initial focuses of the publisher once the app or game has been launched. And it remains a significant focus throughout most of the lifecycle of any endeavor.

This blog was written in partnership with Navdeep Alam, Senior Director, Global Data Warehouse, IQVIA Healthcare is unique. It isn’t defined like other businesses by how much revenue can be generated, but more in terms of achieving positive health outcomes, better value, and saving lives through the rapid development of new treatments and therapies.

When people ask you what EMR means, how many hospitals are in the area, or what’s the best way to understand your patients, you tell them “google it.” But when they ask you how to do something—track your heart rate, book an appointment, pay bills, consult with a doctor online—you say “you know, there’s an app for that.” Because most likely, there is. There’s an app for almost everything these days—and the healthcare industry is no exception.

With Fivetran, Intercom centralizes its Zuora data into Redshift and joins it with product, marketing, sales and additional financial data for reports and dashboards.

Aside from ensuring each service is working properly, one of the most challenging parts of managing a cloud-based infrastructure is cost monitoring. There are countless services to keep track of—including storage, databases, and computation—each with their own complex pricing structure. Monitoring cloud costs is quite different from other organizational costs in that it can be difficult to detect anomalies in real-time and accurately forecast monthly costs.

The Cloudera Operational Database (COD) experience is a managed dbPaaS solution which abstracts the underlying cluster instance as a Database. It can auto-scale based on the workload utilization of the cluster and will be adding the ability to auto-tune (better performance within the existing infrastructure footprint) and auto-heal (resolve operational problems automatically) later this year.

A Shift Towards Industry 4.0 Is Improving Manufacturing Efficiency And Increasing Innovation In Part II of our series with Michael Ger, Managing Director of Manufacturing and Automotive at Cloudera, he looks in greater detail at how AI, big data, and machine learning are impacting connected living and the evolution of autonomous driving.

Here's why you need to double down on your DataOps before your vacation. In the past few months, everything has changed at work (or at home). Q1 plans were scrapped. Reset buttons were smashed. It was all about cost-cutting and keeping lights on. Many app and data teams sought quick solutions and developed workarounds to data challenges and operational problems as people prepared to work from home for the foreseeable future. And now, it’s time for a holiday.

How can you avoid wasting engineering cycles around data pipeline creation and maintenance? Answer: Powered By Fivetran

When migrating a data warehouse to BigQuery, one of the most critical tasks is mapping existing user permissions to equivalent Google Cloud Identity and Access Management (Cloud IAM) permissions and roles. This is especially true for migrating from large enterprise data warehouses like Teradata to BigQuery. The existing Teradata databases commonly contain multiple user-defined roles that combine access permissions and capture common data access patterns.

Cloudera’s Operational Database provides unparalleled scale and flexibility for applications, enabling enterprises to bring together and process data of all types and from more sources, while providing developers with the flexibility they need. In this blog, we’ll look into capabilities that make Operational Database the right choice for hyperscale.

No-code is growing quickly. More and more people from all roles and departments are using a wide range of new tools and apps to build and experiment without fighting for engineering resources. This creates amazing opportunities for growth, but it also means data teams have to integrate data from ever-more sources to get a full picture of their business’s operations.

Next to the healthcare system, COVID-19’s biggest infrastructural burden fell upon the supply chain. Fluctuations in supply and demand of essential goods, along with the oil surplus, led to a freight cliff in mid-April. Outbound tender volume and spot rates bottomed out, which highlighted a massive drop in demand. As the market rebounds, technological investments are key to the industry’s recovery.

Since the financial crisis of 2008, regulators have been consistently working to identify emerging risks that can potentially result in financial stability events. The growth in cloud adoption across the Financial Services Industry (FSI) and the associated increase in reliance on third-party infrastructure providers has gained the attention of regulators at global, regional, and national levels.

Approximately 300,000 restaurants and food service operators across the United States rely on US Foods as their national distributor for food and supplies. In return, US Foods takes a holistic approach to servicing customers by going beyond food offerings. The distributor provides a comprehensive suite of e-commerce technology and business solutions that help restaurants manage their entire business.

Every researcher or machine learning enthusiast faces that well-known experiment management nightmare; it’s usually a rude awakening discovered at the beginning of one’s career. Here’s how it goes.

Fivetran is the industry leader of fully managed data integration from lots of different sources to your data warehouse. There are hundreds of popular sources, like Salesforce, Zendesk, NetSuite, and more, for which Fivetran has fully managed, prebuilt connectors that can deliver your data to your warehouse within literal minutes. However, people often struggle to get data from more obscure sources since most data integration tools focus on what is popular.

Cloud computing is a well-established IT option for businesses of all sizes in the modern era, but there are still plenty of organizations that have remained reticent about adoption. If you are in this camp, here is a look at a few of the reasons that migrating an on-premise data warehouse to the cloud may make a lot of sense, in addition to the other contexts in which the cloud could be a better fit for your business.

Connected Manufacturing’s Pivot to an Enterprise Data Solution Connected Manufacturing is at a turning point and it is catalyzed by a real, measurable change and shift in data types – real-time and time-series data is growing 50% faster than latent or static data forms and streaming analytics projected to grow at a 28% CAGR, leaving legacy data platforms that specialize in static historical data solutions, functioning on-prem or in discrete clouds, inadequate in addressing today’s rea

In Part 1 and Part 2 of this blog post series, Snowflake Service Account Security, discussed service accounts threats and how to mitigate those threats with Snowflake features. Part 3 demonstrates how to manage credential rotation with a sample Hashicorp Vault plugin. You can use many platforms to achieve similar results. The important thing is to understand the patterns used to apply these controls to protect your service accounts.

Today’s an exciting day in the world of data warehousing and analytics for enterprises that want to get more insight from their SAP data. Let’s face it – basically every enterprise on the planet that has SAP wants more value from their platform and data.

Today, we are introducing BigQuery Omni, a flexible, multi-cloud analytics solution that lets you cost-effectively access and securely analyze data across Google Cloud, Amazon Web Services (AWS), and Azure (coming soon), without leaving the familiar BigQuery user interface (UI). Using standard SQL and the same BigQuery APIs our customers love, you will be able to break down data silos and gain critical business insights from a single pane of glass.

“In today’s world of disruption and transformation, there are a few key things that all organizations are trying to figure out: how to remain relevant to their customer base, how to deal with the pressure of disruption in their industry and, undoubtedly, how to look to technology to help deliver a better service.” Paul Mackay Today we are sitting down with Marc Beierschoder, Analytics & Cognitive Offering Lead at Deloitte Germany and Paul Mackay, the EMEA Cloud Lead at Cloudera to dis

Effectively bringing machine learning to production is one of the biggest challenges that data science teams today struggle with. As organizations embark on machine learning initiatives to derive value from their data and become more “AI-driven” or “data-driven”, it’s essential to find a faster and simpler way to productionize machine learning projects so that they can make business impact faster.

Learning additional languages is a common practice in the Netherlands. In primary school, we learn English and secondary school offers French, German, and a host of other options. Learning a new language and speaking it well is tricky.

As a BI Analyst, have you ever encountered a dashboard that wouldn’t refresh because other teams were using it? As a data scientist, have you ever had to wait 6 months before you could access the latest version of Spark? As an application architect, have you ever been asked to wait 12 weeks before you could get hardware to onboard a new application?

The COVID-19 pandemic is forcing every business to see the world differently. From examining business continuity plans, modernizing workforce plans, or building supply chain resiliency, no facet of business has gone untouched. As organizations combat the economic fallout now and in the coming years, agility has never been more important. The key to remaining agile is a better use of data.

Improve your Asana task management for more efficient projects.

This blogpost will cover how customers can migrate clusters and workloads to the new Cloudera Data Platform – Data Center 7.1 (CDP DC 7.1 onwards) plus highlights of this new release. CDP DC 7.1 is the on-premises version of Cloudera Data Platform.

The benefits of building a DevOps culture for software companies are clear. DevOps practices integrate once-siloed teams across the software development lifecycle, from Dev to QA to Ops, resulting in both faster innovation and improved product quality. As a result, most software development teams have deployed tools to enable DevOps practices across their workflow.

Agriculture (Ag) is the oldest and largest industrial vertical in the world, and its importance continues to grow as it becomes more challenging for people to access healthy and fresh food. A recent Agriculture Analytics Market report, released by Markets and Markets, estimates that by 2023, the global agriculture analytics market size will grow from 585 million to 1.2 billion dollars as demands for real-time data analysis and improved operations increase.

With the proliferation of tools generating more available data to be collected, it’s becoming increasingly more and more important to automate your data pipeline to help you get to insights faster. GigaOm recently ran a report comparing a few automated data integration vendors, including Fivetran.

This article gives you an overview of Cloudera’s Operational Database (OpDB) performance optimization techniques. Cloudera’s Operational Database can support high-speed transactions of up to 185K/second per table and a high of 440K/second per table. On average, the recorded transaction speed is about 100K-300K/second per node. This article provides you an overview of how you can optimize your OpDB deployment in either Cloudera Data Platform (CDP) Public Cloud or Data Center.

Powered by Fivetran (PBF) is a new offering for modern data insights platforms that provide analytics-as-a-service companies. These firms build data products on top of disparate solutions such as Tableau, Snowflake and Redshift, and offer insights to decision-makers in diverse verticals, from finance and marketing to energy and transportation.

Today, we’re announcing Data QnA, a natural language interface for analytics on BigQuery data, now in private alpha. Data QnA helps enable your business users to get answers to their analytical queries through natural language questions, without burdening business intelligence (BI) teams. This means that a business user like a sales manager can simply ask a question on their company’s dataset, and get results back that same way.

As organizations look to get smarter and more agile in how they gain value and insight from their data, they are now able to take advantage of a fundamental shift in architecture. In the last decade, as an industry, we have gone from monolithic machines with direct-attached storage to VMs to cloud. The main attraction of cloud is due to its separation of compute and storage – a major architectural shift in the infrastructure layer that changes the way data can be stored and processed.

In the lifecycle of a data warehouse in production, there are a variety of tasks that need to be executed on a recurring basis. To name a few concrete examples, scheduled tasks can be related to data ingestion (inserting data from a stream into a transactional table every 10 minutes), query performance (refreshing a materialized view used for BI reporting every hour), or warehouse maintenance (executing replication from one cluster to another on a daily basis).

The value of YouTube has grown significantly for companies looking to bolster their brands with video content. The YouTube API is report-based, and its prebuilt reports fall into one of two categories: channel reporting and content owner reporting. Channel reports refer to the videos on a specific YouTube channel, while content owner reports contain data on all the channels owned by a particular individual.

Copyright quickly migrates its on-prem databases into Snowflake cloud using Fivetran. By implementing the modern data stack, and decommissioning numerous legacy platforms, Copyright estimates a savings of $40,000 a year and finds it much easier to manage the workload.

Our 1.2.0.0 release of Cloudera Streaming Analytics Powered by Apache Flink brings a wide range of new functionality, including support for lineage and metadata tracking via Apache Atlas, support for connecting to Apache Kudu and the first iteration of the much-awaited FlinkSQL API. Flink’s SQL interface democratizes stream processing, as it caters to a much larger community than the currently widely used Java and Scala APIs focusing on the Data Engineering crowd.

In these uncertain times of the COVID-19 crisis, one thing is certain – data is key to decision making, now more than ever. And, the need for speed in getting access to data as it changes has only accelerated. It’s no wonder, then, that organisations are looking to technologies that help solve the problem of streaming data continuously, so they can run their businesses in real-time.

In recent years, there’s been an increasing demand for data scientists left and right, across industries and across departments. In the same vein, companies are getting more and more data than they know what to do with. In fact, according to IBM, 90% of the data in the world today has been created in the last two years alone. To put this influx to good use, organizations are turning to data scientists.

The resurrection of AI due to the drastic increase in computing power has allowed its loyal enthusiasts, casual spectators, and experts alike to experiment with ideas that were pure fantasies a mere two decades ago. The biggest benefactor of this explosion in computing power and ungodly amounts of datasets (thank you, internet!) is none other than deep learning, the sub-field of machine learning(ML) tasked with extracting underlining features, patterns, and identifying cat images.

Research on COVID-19 is being produced at an accelerating rate, and machine intelligence could be crucial in helping the medical community find key information and insights. When I came across the COVID-19 Open Research Dataset (CORD-19), it contained about 57,000 scholarly articles. Just one month later, it has over 158,000 articles. If the clues to fighting COVID-19 lie in this vast repository of knowledge, how can Qlik help?

Over the last 100 years alone, artificial intelligence has achieved what was once believed to be science fiction: cars that drive themselves, machine learning models that diagnose heart disease better than doctors can, and predictive customer analytics that lead to companies knowing their customers better than their parents do. This machine learning revolution was sparked by a simple question: can a computer learn without explicitly being told how?

At Google Cloud, we work with organizations performing large-scale research projects. There are a few solutions we recommend to do this type of work, so that researchers can focus on what they do best—power novel treatments, personalized medicine, and advancements in pharmaceuticals.

We hear from our users in the scientific community that having the right technology foundation is essential. The ability to very quickly create entire clusters of genomics processing, where billing can be stopped once you have the results you need, is a powerful tool. It empowers the scientific community to spend more time doing their research and less time fighting for on-prem cluster time and configuring software.

In this release, we’ve focussed on providing exceptional capability for our customers - both enterprise and software companies - to create and deploy embedded analytics experiences that drive user adoption with minimum amount of effort and coding.

As first-party customer data continues to explode, companies have struggled to make it actionable for personalization, advanced analytics, and other business purposes. As a result, customer data platforms that consolidate and activate known customer information have emerged to help companies generate ROI from their data. At present, nearly 80% of marketing organizations already have a customer data platform or are developing one.