As we covered in part 1 of this blog series, Snowflake’s platform is architecturally different from almost every traditional database system and cloud data warehouse. Snowflake has completely separate compute and storage, and both tiers of the platform are near instantly elastic. The need to do advanced resource planning, agonize over workload schedules, and prevent new workloads on the system due to the fear of disk and CPU limitations just go away with Snowflake.

Today SEO is much more than just finding high converting keywords for better ranking. Most marketers and content writers nowadays rely on different strategies to stay in the game. Imagine handling such intricate tasks manually or shuffling through several tools daily to get this done. Sounds hectic, right? But what if we told you, there’s a single package out there to make your work easier.

Let’s precisely define the different kinds of data repositories to understand which ones meet your business needs. October 29, 2020 A data repository serves as a centralized location to combine data from a variety of sources and provides users with a platform to perform analytical tasks. There are several kinds of data repositories, each with distinct characteristics and intended use cases. Let’s discuss the peculiarities and uses of data warehouses, data marts and data lakes.

With the massive explosion of data across the enterprise — both structured and unstructured from existing sources and new innovations such as streaming and IoT — businesses have needed to find creative ways of managing their increasingly complex data lifecycle to speed time to insight.

As a product feature for your app, embedded analytics is undoubtedly a valuable tool. But historically, many product managers and software developers have approached it as a standalone capability. This has led to dashboards and reporting modules added as an afterthought, rather than as a founding strategic component of the core application.

We’ve all heard the war stories born out of wrong data: These stories don’t just make you and your company look like fools, they also cause great economic damages. And the more your enterprise relies on data, the greater the potential for harm. Here, we take a look at what data quality is and how the entire data quality management process can be improved.

Cloudera services logs offer a breadth of information to assist in cluster maintenance; from assisting in security checks, auditing tasks, and validation for performance tuning and testing tasks – to name a few. However, log records generated by these services do not hold the same value for every organisation.

The customer has never been more right. Across industries, customers have become conditioned to demand not only near-instant responses to their needs but that their needs be anticipated in advance. Financial institutions are not given a pass, despite a competitive landscape flooded with regulation and privacy considerations. The customer still has expectations for a personalized, timely, and relevant experience.

The push to embrace cloud-based technologies has undoubtedly transformed IT infrastructures at every level of government. Federal, state, and local agencies have made significant strides in modernizing how data is collected, stored, and analyzed, all in service of their mission and in fulfillment of strategic IT mandates.

At Cloudera Fast Forward we work to make the recently possible useful. Our goal is to take the incredible data science and machine learning research developments we see emerging from academia and large industrial labs, and bridge the gap to products and processes that are useful to practitioners working across industries.

Broadcasting is now available in this release for both dashboards and presentations. Just like reports, you can now enable scheduled delivery of these analytic content to different audiences. We have also included additional options for schedules — making it more granular for specific frequencies. For example, for fortnightly broadcasts I can now set the delivery to be on the second Monday. Or for monthly broadcasts, to have deliveries happen on the fifth day every month.

The telco industry is ripe with application security risks—find out why. The telecommunications industry has seen its fair share of cyberattacks over the years, and these are only going to grow in frequency and sophistication. Without a robust cybersecurity strategy in place, these vulnerabilities will persist. This is why security should be an ongoing effort in any telecom organization, and vulnerabilities must be systematically addressed to ensure protection.

The infamous data science workflow with interconnected circles of data acquisition, wrangling, analysis, and reporting understates the multi-connectivity and non-linearity of these components. The same is true for machine learning and deep learning workflows. I understand the need for oversimplification is expedient in presentations and executive summaries. However, it may paint unrealistic pictures, hide the intricacies of ML development and conceal the realities of the mess.

Growing up, were you ever told you can’t have it all? That you can’t eat all the snacks in one sitting? That you can’t watch the complete Back to the Future trilogy as well as study for your science exam in one evening? Over time, we learn to set priorities, make a decision for one thing over the other, and compromise. Just like when it comes to data access in business.

Cloudera and Dell/EMC are continuing our long and successful partnership of developing shared storage solutions for analytic workloads running in hybrid cloud.

We are delighted to being invited again to Germany’s leading embedded software conference the ESE Kongress. We will be focusing on the MISRA language standard as commonly used in the automotive, avionics and wider safety critical space and educate about the importance of MISRA Compliance as defined in 2020. MISRA can be overwhelming for new projects and it is essential to understand the constraints as well as the freedoms regarding MISRA compliance.

Computer science loves abstraction, and now, as it turns out, so does data management. Abstraction means reducing something complex to something simpler that elegantly delivers its essence. Applications all over the world become more robust and easier to maintain and evolve when a simple interface is put in front of a complex service. The consumer of the service is able to say: This is a lot simpler than allowing the consumer to reach directly under the hood and mess with the engine.

Many companies do them, and you will see them scattered everywhere. Our ambition around trends is different though. We want to contextualize and embed trends into a broader theme, capturing the zeitgeist. That way the strands become interwoven into a fabric, showing a structural pattern and hopefully also indicating where we are heading.

Learn the how and what of analytics and data integration.

Learn how to get your organization started with automated data integration.

Replication (covered in this previous blog article) has been released for a while and is among the most used features of Apache HBase. Having clusters replicating data with different peers is a very common deployment, whether as a DR strategy or simply as a seamless way of replicating data between production/staging/development environments.

An exceptional embedded analytics offering is underpinned by the right strategy and framework - and this starts with a clear vision. To maximize the value of data assets, you may need to recognize and then address where your product may need to improve it’s BI maturity level. To do this, it’s time to focus on where your analytics development capability and tooling is today.

Unravel Data helps a lot of customers move big data operations to the cloud. Chris Santiago is Global Director of Solution Engineering here at Unravel. So Unravel, and Chris, know a lot about what can make these migrations fail. Chris and intrepid Unravel Data marketer Quoc Dang recently delivered a webinar, Reasons why your Big Data Cloud Migration Fails and Ways to Overcome. You can view the webinar now, or read on to learn more about how to overcome these failures.

Incredibly excited about today’s news that Qlik has acquired Blendr.io. Blendr.io’s easy-to-use, scalable and secure embedded integration and automation platform (iPaaS) will expand our ability to deliver on our vision of Active Intelligence, where real-time, up-to-date data triggers immediate action to accelerate business value across the entire data and analytics supply chain.

As businesses increasingly leverage data-driven decision making, the ability to use and understand data at the company-wide level becomes mission critical. While tech behemoths like Netflix, Airbnb, and Spotify have strong data cultures built over the last decade, most companies often face challenges getting up and running with data.

The Insurance industry is in uncharted waters and COVID-19 has taken us where no algorithm has gone before. Today’s models, norms, and averages are being re-written on the fly, with insurers forced to cope with the inevitable conflict between old standards and the new normal.

The only certainty in today’s world is change. And nowhere is that more apparent than in the way organizations consume data. A typical company might have thousands of analysts and business users accessing dashboards daily, hundreds of data scientists building and training models, and a large team of data engineers designing and running data pipelines. Each of these workloads has distinct compute and storage needs, and those needs can change significantly from hour to hour and day to day.

It’s time, again, to look at a chart that you might not be using, but that you definitively should consider using when doing data visualization. The waterfall chart is great at visualizing the cumulative effect from positive and negative changes, as, for example, you would see in a Profit and Loss (P&L) report.

On average, data-driven companies grow more than 30% every year. Because of the competitive advantage that data confers to incumbents who are capable of extracting value from it, it has been called the new oil. Companies are tapping into this well of resources because of the advantages that it has to offer: But using data to run your operations poses its own set of challenges.

Today we are introducing a new series of blog posts that will take a look at recent enhancements to Apache Impala. Many of these are performance improvements, such as the feature described below which will give anywhere from a 2x to 7x performance improvement by taking better advantage of all the CPU cores. In addition, a lot of work has also been put into ensuring that Impala runs optimally in decoupled compute scenarios, where the data lives in object storage or remote HDFS.

How can your organization ensure data access and transparency? Measure yourself against this model, courtesy of our partners at Slalom.

For enterprise applications and startups to scale, they need to manage large volumes of data in real-time. Customers must have the ability to search for any product or service from your database within seconds. When you manage a relational database, data is spread across multiple tables. So, customers may experience lag during search and data retrieval. However, this is different with Elasticsearch and other NoSQL databases.

Today, companies can measure every aspect of business health, except the health of their data which drives business decisions. Data is vital to inform critical decisions such as identifying new routes to market, systems to support business agility, and more resilient supply chains. As Harvard Business Review puts it, “Your organization’s data is the source of both the opportunity and the challenges to your innovation.

Data architecture is a hot topic right now. And rightfully so. Technological advances bring out a myriad of new solutions that go beyond the traditional relational databases and data warehouses. They enable companies to accelerate their entire data pipeline (or at least remove painful bottlenecks) and shorten the analytic cycles. The portfolio of data assets managed by companies is also growing.

Audio signals are all around us. As such, there is an increasing interest in audio classification for various scenarios, from fire alarm detection for hearing impaired people, through engine sound analysis for maintenance purposes, to baby monitoring. Though audio signals are temporal in nature, in many cases it is possible to leverage recent advancements in the field of image classification and use popular high performing convolutional neural networks for audio classification.

Our latest dbt (data build tool) package helps accelerate your Mixpanel event analytics.

At Google Cloud, we’re invested in building data analytics products with a customer-first mindset. Our engineering team is thrilled to share recent feature enhancements and product updates that we’ve made to help you get even more value out of BigQuery, Google Cloud’s enterprise data warehouse.

We live in a Fourth Industrial Revolution, where data is the lifeblood of business. Those of us who harness the power of artificial intelligence, machine learning and augmented analytics to uncover insights from data are the ones who will be able to find better ways of driving efficiency, productivity and superior business outcomes.

The data age has been marked by numerous “hype cycles.” First, we heard how Big Data, Data Science, Machine Learning (ML) and Advanced Analytics would have the honor to be the technologies that would cure cancer, end world hunger and solve the world’s biggest challenges. Then came third-generation Artificial Intelligence (AI), Blockchain and soon Quantum Computing, with each one seeking that honor.

Through automated data pipelines, analysts can access the data they need and engineering teams can complete higher-value projects.

Looking for a Segment and Fivetran comparison? This article covers some fundamental differences between a specialized data integration tool and a customer data platform.

Data Discovery and Exploration (DDE) was recently released in tech preview in Cloudera Data Platform in public cloud. In this blog we will go through the process of indexing data from S3 into Solr in DDE with the help of NiFi in Data Flow. The scenario is the same as it was in the previous blog but the ingest pipeline differs. Spark as the ingest pipeline tool for Search (i.e.

The new normal has changed the way we work and the way we conduct business. More and more employees are working from home, customers are shopping online, and everyone’s phone is still attached to their ears. Bottom line: everything we’re doing in business and in our personal lives is leaving a digital trail. In fact, now devices are getting in the game and creating more data than people, 277 times more, according to Cisco.

The COVID-19 pandemic has changed nearly everything. It’s affected nearly all Americans, and as such, it’s impacted every organization they interact with, both B2C and B2B. One industry that has had its operations turned upside down is the grocery industry. Grocery stores and their consumer packaged goods (CPG) suppliers and partners had to improvise and adapt nearly overnight to accommodate the changing demands of shoppers.

Every professional has used Microsoft products in their work life. The ubiquity of Microsoft Office in the enterprise is one of the main reasons many analytics users are familiar with Power BI, given how the company bundles its business intelligence software with other licenses in the same way Excel and PowerPoint comes with Office.

Over the past few months, we’ve been considering how to create a platform that’s accessible to everyone. With that said, we’re happy to announce that you can now use Keboola Connection for free! No contract, no talking to our (albeit incredibly lovely) sales team - just jump in and start building.

The design and training of neural networks are still challenging and unpredictable procedures. The difficulty of tuning these models makes training and reproducing more of an art than a science, based on the researcher’s knowledge and experience. One of the reasons for this difficulty is that the training procedure of machine learning models includes multiple hyperparameters that affect how the training process fits the model to the data.

In a Slack discussion, the two CEOs explain why the Fivetran-dbt integration is great for data analytics engineering enthusiasts. After the recent launch of Fivetran dbt Transformations, both the Fivetran and Fishtown Analytics teams received questions about the newly available feature. (Fishtown is the team behind dbt.) Fivetran CEO George Fraser and Fishtown Analytics CEO Tristan Handy addressed those questions on Slack, and discussed the harmonious relationship between the two companies.

Growing heavy civil construction business brings on a modern data stack of Fivetran, BigQuery and Looker to gain a competitive edge. Want to hear more from Emery Sapp & Son's Clayton Hicklin? Join him and a number of other incredible data professionals at the 2020 Modern Data Stack Conference October 21-22. Register here.

Understanding the data we collect is essential—it allows us to identify trends and uncover answers about our world. However, stories in our data frequently go untold. Large datasets are hard to share between research communities due to their size, security restraints, and complexity. Even if these datasets are accessible to users, the tools needed to query them often require deep technical knowledge.

October happens to be the month to celebrate World Smile Day when Harvey Ball, the inventor of the smiley face declared this day as such to give people a reason to smile. This month, BigQuery users have a lot of new reasons to smile about with the release of new user-friendly SQL capabilities now generally available.

Running a large commercial airline requires the complex management of critical components, including fuel futures contracts, aircraft maintenance and customer expectations. Airlines, in just the U.S. alone, average about 45,000 daily flights and transporting over 10 million passengers a year (source: FAA). Airlines typically operate on very thin margins, and any schedule delay immediately angers or frustrates customers.

Apache Spark unifies batch processing, real-time processing, stream analytics, machine learning, and interactive query in one-platform. While Apache Spark provides a lot of capabilities to support diversified use cases, it comes with additional complexity and high maintenance costs for cluster administrators. Let’s look at some of the high-level requirements for the underlying resource orchestrator to empower Spark as a one-platform.

Customers of B2B companies rely on insights from applications to grow their business, secure their infrastructure, make business decisions, and more. Unless your B2B company offers a rich set of analytics within its product, your customers likely demand nightly data dumps from your application so they can analyze application data with their own BI stack.

Virtually every marketing organization is taking steps to become more data-driven, but there are considerable gaps between vision and reality. According to a 2018 Salesforce report, only 47% of marketers have a completely unified view of customer data sources. Meanwhile, customer data complexity is only increasing. According to Salesforce’s 2020 “State of Marketing” study, the median number of data sources leveraged by marketers is projected to jump by 50% between 2019 and 2021.

Tapping into more compute power is the next frontier of data science. Data scientists need it to complete increasingly complex machine learning (ML) and deep learning (DL) tasks without it taking forever. Otherwise, faced with a long wait for compute jobs to finish, data scientists give in to the temptation to test smaller datasets or run fewer iterations in order to produce results more quickly.

Much has been written on the growth of machine learning and its impact on almost every industry. As businesses continue to evolve and digitally transform, it’s become an imperative for businesses to include AI and ML in their strategic plans in order to remain competitive. In Competing in the Age of AI, Harvard professors Marco Iansiti and Karim R. Lakhani illustrate how this can be confounding for CEOs, especially in the face of AI-powered competition.

Modern business applications leverage Machine Learning (ML) and Deep Learning (DL) models to analyze real-world and large-scale data, to predict or to react intelligently to events. Unlike data analysis for research purposes, models deployed in production are required to handle data at scale and often in real-time, and must provide accurate results and predictions for end-users.

Data science has come a long way, and it has changed organizations across industries profoundly. In fact, over the last few years, data science has been applied not for the sake of gathering and analyzing data but to solve some of the most pertinent business problems afflicting commercial enterprises.

We’re delighted to announce the release of the Iguazio Data Science Platform version 2.8. The new version takes another leap forward in solving the operational challenge of deploying machine and deep learning applications in real business environments. It provides a robust set of tools to streamline MLOps and a new set of features that address diverse MLOps challenges.

Recently, my colleague published a blog build on your investment by Migrating or Upgrading to CDP Data Center, which articulates great CDP Private Cloud Base features. Existing CDH and HDP customers can immediately benefit from this new functionality. This blog focuses on the process to accelerate your CDP journey to CDP Private Cloud Base for both professional services engagements and self-service upgrades.

Since Snowflake announced general availability on Azure in November 2018, increasing numbers of customers are deploying their Snowflake accounts on Azure, and with this, more customers are using Power BI as the data visualization and analysis layer. As a result of these trends, customers want to understand the best practices for a successful deployment of Power BI with Snowflake.

COVID-19 has radically altered most aspects of our social worlds and the way we do business. One of the largest impacts to organizations has gone on mostly unseen to the outside world. Companies have been quickly altering the way they manage the keys to their data kingdoms to maximize their data’s value to survive and compete.

Digitalization and the rapid technological development have resulted in an abundance of data across industries, and this volume is only expected to increase. To extract insights from “big data” – data sets that are too large or complex for traditional data processing technologies – organizations need sheer processing power, raw storage, and strong data analytics capabilities.

We are thrilled to announce that Cloudera has acquired Eventador, a provider of cloud-native services for enterprise-grade stream processing. Eventador, based in Austin, TX, was founded by Erik Beebe and Kenny Gorman in 2016 to address a fundamental business problem – make it simpler to build streaming applications built on real-time data. This typically involved a lot of coding with Java, Scala or similar technologies.

A few years ago, we got an evaluation form from a prospective customer wanting to understand more about what we do. In the comments they wrote... “Please don't chase me down like those bastards at Sisense” That little nugget of gold told me a lot about the way our buyers want to purchase software - everyone hates talking to a salesperson too early. So we decided to turn our sales process on its head and put all of our technical evaluation content online.

Data scientists and engineers at the online data science education platform focus on attribution, customer lifetime value and the ideal customer profile.

Make enterprise data more accurate, and instantly actionable, by adding automated data integration to your stack. Today’s enterprises and medium-sized companies are looking to ensure that critical business decisions are guided by rigorous data analysis. They have scaled up their analytics teams (composed of data engineers, data scientists and data analysts), and their IT departments have tried to meet the needs of those teams.

Digital transformation is not just about technological transformation of the organization, it’s about transforming the culture of an organization. It’s not enough to bolt technology onto an existing strategy and consider it transformed. That’s the message from our Chief Marketing Officer Mick Hollison discussing digital transformation with Charlene Li at Cloudera Now.

However, the complexity and volume of data every business accumulates is a common challenge for those that need to make decisions. Data is a continuous and always growing consideration, and it can be particularly challenging for larger enterprises.

We missed it, again. But, could we have seen it, even with advancements in data analytics? Many people have referred to COVID-19 as an unexpected black swan event, after Nassim Taleb's famous book, which I’m rereadinging as I write this post. A black swan is an unpredictable event that is beyond what is normally expected of a situation and has potentially severe consequences.

Automated data integration can help you jumpstart your machine learning efforts. Learn about the modern data science stack. It’s an oft-cited factoid that data scientists spend only 20% of their time doing actual data science work, while the rest of their time is spent on doing what is often delightfully referred to as “data munging” — that is, obtaining, cleaning, and preparing data for analysis.

Timely, accurate and trusted data has never been more important than it is now during this pandemic. Since late summer, many areas across the UK have had more stringent restrictions imposed to reflect the growing number of cases. Similarly, Test and Trace uses information on who we’ve been in contact with to provide guidance for when we should self-isolate, which in turn helps us personally manage the risk to those around us.

For decades, the analytics/BI community has suffered from low user adoption (~30%). Dashboards and fancy visualizations have only proven to be the starting point of the analytics journey, not the endpoint. We are living in a world that demands far greater agility than a fixed layer of information or KPI can provide. Poor user adoption is the result when the analytics system fails to support the users full journey: from data – to insight – to action.

When it comes to cloud, being an early adopter does not necessarily put you ahead of the game. I know of companies that have been perpetually “doing cloud” for 10 years, but very few that have “done cloud” in a way that democratises and makes data accessible, with minimal pain points. Cloud is an enabler. It makes it easier to collect, analyse, and disseminate information.

The COVID-19 pandemic has shone a bright light on the need for flexible and engaging leadership. Most of the recent leadership-themed articles focus on how to bridge the remote-only workplace relationship gap, the importance of increased engagement and empathy, and the need to provide technology that empowers and provides a more equitable work/life balance.

Gartner names Fivetran in the 2020 Gartner Magic Quadrant in the data integration tools category. Today, we’re proud to share that Fivetran has been recognized in the August 2020 Gartner Magic Quadrant for Data Integration Tools for the first time. The data integration market is rapidly evolving and expanding as organizations increasingly understand the value of analytics and the importance of data-backed decisions in driving business success.

Fivetran or Stitch for automated ELT? Learn the pros & cons of each platform. October 5, 2020 We commonly hear from our customers that when they initially evaluated cloud data integration solutions, both Fivetran and Stitch came up in their cursory searches. With the Stitch's announcement of their free plan deprecation, we’ve received an influx of questions on how the two solutions differ. Below, we highlight the differences in the approaches to handling automated ELT as well as pricing.

With the pandemic forcing businesses worldwide to reboot, many have no choice but to exact drastic cost-cutting measures to keep the lights on. Cloud computing is an expense incurred by every digital business that, unlike many other operating costs, is largely variable.

Cloudera Data Platform Powered by NVIDIA RAPIDS Software Aims to Dramatically Increase Performance of the Data Lifecycle Across Public and Private Clouds Cloudera announced today a new collaboration with NVIDIA that will help Cloudera customers accelerate data engineering, analytics, machine learning and deep learning performance with the power of NVIDIA GPU computing across public and private clouds.

So, if you’re a nose-to-the-keyboard developer, there’s ample probability that this analogy is outside your comfort zone … bear with me. Imagine two Olympics-level figure skaters working together on the ice, day in and day out, to develop and perfect a medal-winning performance. Each has his or her role, and they work in sync to merge their actions and fine-tune the results.

This is the second post in a series about data modeling and data governance in the cloud from Snowflake’s partners at erwin. See the first post here. As you move data from legacy systems to a cloud data platform, you need to ensure the quality and overall governance of that data. Until recently, data governance was primarily an IT role that involved cataloging data elements to support search and discovery.

In recent years, organizations have been making massive investments in data analytics to transform their growing volume of data into actionable insights to inform decision-making. However, the pursuit of becoming data-driven has uncovered challenges earlier in the data pipeline that are preventing companies from reaping all the benefits from their data.

Since 2013 the UK Government’s flagship ‘Cloud First’ policy has been at the forefront of enabling departments to shed their legacy IT architecture in order to meaningfully embrace digital transformation. The policy outlines that the cloud (and specifically, public cloud) be the default position for any new services; unless it can be demonstrated that other alternatives offer better value for money.

Software vendors often ask me when is the ideal time to swap out a legacy analytics solution that they’ve embedded into their application. There's five key signs that can tell you your embedded solution isn’t right anymore and it’s time to switch.

In August 2020, Snowflake announced several new features, all in preview, that make its cloud data platform easier to use, more powerful for sharing data, and more usable via Snowflake-supported languages. These innovations mean you can bring more workloads, more users, and more data to Snowflake, helping your organization solve your most demanding analytics challenges. Multi-Cloud, Cross-Cloud, and Pattern-Matching Support in Snowpipe