As both Amazon Redshift and Google BigQuery are petabyte-scale, cloud-based data warehouses, they both possess their own set of parameters when tackling end-to-end security for their customers. Data warehouses require a flexible and powerful security infrastructure and operate with scalable requirements. Both Amazon Redshift and Google BigQuery (alongside their parent ecosystems) take security very seriously but handle it in different ways.
With a cloud-based data warehouse, there’s no physical infrastructure to manage, allowing for a streamlined focus on analytics and insights, rather than hours of manual maintenance. But, like any system, every data warehouse needs to undergo maintenance for a tune up from time to time.
In this blog post, we’ll cover the crucial differences in how Amazon Redshift and Google BigQuery perform maintenance. For many companies, maintenance is a point of contention as it’s a leading indicator of overall data warehouse performance.
In evaluating a data warehouse, it’s important to consider how data loads from your database into the data warehouse. Second, it’s critical to also consider the speed, accessibility and latency once data is loaded into the data warehouse.
Much like how cloud-based data warehouses revolutionized warehousing, a solution to data loading can be found through an ETL service via companies or in-house built tools. However, there are differences in loading data into Amazon Redshift and Google BigQuery.
Data warehouses are architected to handle a large volume of data. In fact, many companies use data warehouses to store historical data going back at least three years—and this is a great practice when it comes to enriching information for a target persona or running product usage analysis.
As the overall volume of data grows, it’s not uncommon for a data warehouse to perform operational checks. Today, we’re analyzing data warehouse operations in four separate parts: provisioning, loading, maintenance and security. In this blog post, we'll cover provisioning.
Stay tuned as we continue to publish our full analysis on data warehouse operations between Amazon Redshift and Google BigQuery.
Interactive reporting is crucial for businesses. In enabling everyone to have access to data, it allows them to engage with information in new ways and ultimately, helps improve job performance.
As a data analyst, empowering business users with interactive reporting frees up your time to perform more sophisticated analyses. However, with the addition of users querying data, it can amount to performance pressures for a data warehouse.
Data warehousing performance is often measured by a multitude of factors and tests. In our ongoing analysis between Amazon Redshift and Google BigQuery, we're comparing how each handle throughput and concurrency.
Data warehouses are not just for storing data, they must be architected to facilitate analytics and business reporting needs, handling complex analytical queries quickly without impacting operational databases or other systems where the data was originally created.
Raw data on its own offers no insights and addresses few business goals, but by loading raw data into a data warehouse, it’s possible to facilitate data exploration, interactive reporting and data-informed decision-making for an entire organization.
Choosing a data warehouse that will help you analyze your data doesn’t have to be difficult. To help you make your data warehouse decision, particularly between Amazon Redshift and Google BigQuery, we’ve written a whitepaper titled “What to Consider When Choosing Between Amazon Redshift and Google BigQuery.”
During a recent webinar, Dan Ahmadi, Director of Growth at Meteor talked about how Meteor uses Segment Sources and Chartio to drive exponential growth. Meteor’s latest open-source project, Apollo, has generated 2.4 million Segment events per week, or four events per second, since its release earlier this year.
At an hourly rate, Meteor is pushing their Segment Sources event data into their Amazon Redshift data warehouse. Once the data has been processed through Amazon Redshift, Meteor can then instantly access and analyze their data within Chartio.
In this blog post, learn what types of data sources Meteor is using and how they create agile dashboards within Chartio.
People turn to data when they have questions and want answers. An integral part of a data analyst’s role is to use data to inform and influence the direction of your company by making sense of data and answering questions. Data is a vital instrument that has a major impact, especially if you get the results in front of high-level decision-makers.
By partnering across your company with teams ranging from Product to Sales to Marketing, you’ll be able to design A/B tests, run experiments, iterate on results and do the lion’s share of understanding patterns and trends in user behavior to find new opportunities for growth. However, to fully realize data’s power, you need to understand each department’s high-level metrics and run queries to answer those metrics.
Today’s ultra-competitive business landscape requires companies to be agile and provide the best service on the market. Luckily, today’s leading companies are collecting more data than previous decades and those with the competitive advantage are using that data to beat the competition.
In this blog post, we’ll walk through the advantages in using a data warehouse for analysis and how data warehouses are a competitive advantage to the overall business.
In our blog post Choosing the Right Database for Your Data Strategy, we provided a strategy in how to choose an operational database that will suit your company’s long-term needs. As an operational database is crucial for processing and storing transactional data, like product usage or contact information, data warehouses play an integral role in turning static data into insights.
Data plays a critical role in running a business. As a data analyst, you understand the importance of data. But sometimes, it can be difficult to turn your technical aptitude into business insights.
As data analysts evaluate user behavior through analytics tools, business users are interested in understanding the performance of their efforts—whether it’s Sales, Marketing or Customer Success.
Much like your company’s data, a data analyst cannot be siloed into his or her own world, running analyses separate from business conversations.
Data is your company’s most valuable asset. In using data, Executives are able to accurately forecast revenue and marketers are able to run efficient campaigns. Without data, we’d all be going at it blind.
To unlock the full reach of your data, you need to make sure it’s extracted, processed, consolidated and transformed into insights. The key here is consolidating data from various sources. So, instead of drudging through different data sources and working in a vacuum, it’s time to bring data together.
Databases are more than columns and rows. Whether you’re a scaling SaaS business or a Fortune 500 company, you’ll need a way to store all your data from email marketing lists to user IDs. Choosing the right database is a long term decision that will impact your business. You don’t want to start out on the path of implementing a database that your business will outgrow in a few months. And, rethinking your entire database strategy down the road isn’t ideal.
I closed-out my first year after receiving my Bachelors Degree, in command of a 3-person team, tasked to regularly go forward of most infantry assets in providing forward reconnaissance, all while carrying well over a couple million dollars-worth of “sensitive” communications gear. I was just a young kid thrust into an extraordinary situation based on the needs of his chain of command.
The business world of today is a far cry from the stresses of the modern battlefield. It’s considerably more beneficial to a person’s health and well-being than a tour in a combat zone, but there is one aspect that is constant in both environments: Decision-Paralysis – over-analyzing a situation to the point that a decision is never taken.
I recently spoke at the community meetup for the MADlib Apache incubator project, an open source library for scalable in-database analytics.
If you've never heard of MADlib and you use PostgreSQL, Greenplum or HAWQ (also an Apache incubator project) then you should definitely check it out. It's a database extension that allows you to perform advanced statistical and machine learning computations within your database where your data resides...
Do you find it hard to concentrate in a disorganized environment? According to Cole Nussbaumer Knaflic of the recently published book Storytelling with data: a data visualization guide for business professionals, 75% of people have a hard time focusing in a cluttered environment and that number increases to 87% when it comes to visualizing data.
What does this mean? A messy dashboard with many colors and charts can seriously affect how your audience interprets the message you are trying to convey.
Check out our post to learn the top 5 tips to decluttering and grabbing your audience's attention.
We’re excited to interview William Chen, Data Scientist at Quora. William also contributes heavily to Quora’s community by writing extensively about Data Science, Statistics, Data Analysis, Machine Learning, and Probability.
If you’re whirling around in the 1.0 world, you might be a fan of measuring dials (dials per hour reps are doing). It's a useful metric when you’re training new reps and one that measures activity. But once a rep ramps up their pipelines, are you still using dials as a way to track results? What about number of appointments? Is the appointment metric important to you? Does that really give you the data you need to more accurately forecast opportunities?
Now that we have covered best practices for creating a framework for your dashboard, this last post will cover how to make your dashboard a beautiful visualization.
Now that we have established the purpose of the dashboard, start thinking about how the dashboard’s layout will best serve the audience and provide value. This is the first step toward thinking about how the dashboard looks and how it will function.