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Seo Master present to you: 
By Michael Manoochehri, Developer Programs Engineer, Cloud Platform
Google BigQuery is designed to make it easy to analyze large amounts of data quickly. Overwhelmingly, developers have asked us for features to help simplify their work even further. Today we are launching a collection of updates that gives BigQuery a greater range of query and data types, more flexibility with table structure, and better tools for collaborative analysis.
Big JOIN and Big Group Aggregations
Extracting insights from multiple datasets can be challenging and time-consuming. This is especially true when datasets become too large to query with a traditional database system. With traditional databases, SQL functions like joining and grouping are typically used to bring together data for analysis. What happens when your data is too large to fit into a conventional database? Working with multi-terabyte datasets often requires developing complicated MapReduce workflows, investing in expensive infrastructure, and great deal of time. Very often, it's a combination of all three.
In response to developer feedback, we're launching new features that enable analysts and developers to run fast SQL-like join and aggregate queries on datasets without the need for batch-based processing. Our new Big JOIN feature gives users the ability to produce a result set by merging data from two large tables by a common key. Big JOIN simplifies data analysis that would otherwise require a data transformation step, by allowing users to specify JOIN operations using SQL.
Popular web applications produce user activity logs that can grow by billions of rows each week. Dividing users into smaller groups is a key step for analysis. However, each group of users can number in the millions. To handle this for such large volumes, we've enabled Big Group Aggregations, which significantly increases the number of distinct values that can be grouped in a result set.
To use these new features, simply add the EACH modifier to JOIN or GROUP BY clauses.
For more information, including best practices, when using JOIN EACH and GROUP EACH BY, visit the BigQuery query reference.
Native support for TIMESTAMP data type
We are also adding a new TIMESTAMP data type, in response to one of our most frequent requests from developers. This new data type lets you import date and time values in formats familiar to users of databases such as MySQL, while preserving timezone offset information.
Along with the new data type come new functions for converting TIMESTAMP fields into other formats, calculating intervals, and extracting components such as the hour, day of week, and quarter.
Read more about the available TIMESTAMP functions in our query reference guide.
Add columns to existing BigQuery tables
When working with large amounts of fast moving data, it's not uncommon to find out that you need to add additional fields to your tables. In response to developer feedback, we have added the ability to add new columns to existing BigQuery tables.
To take advantage of this feature, simply provide a new schema with additional columns using either the "Tables: update" or "Tables: patch" BigQuery API methods.
For more information on this feature, visit the BigQuery API reference.
BigQuery Web UI: Dataset links and dataset sharing notifications
BigQuery has always provided project owners with very fine-grained control of how their datasets are shared. To make it easier for teams to work on collaborative data analysis, we've added direct links to individual datasets in the BigQuery Web UI. This provides a convenient way for authorized users to quickly access a dataset, and allows for bookmarking and sharing.
In addition, we've also added email notifications to inform users when they've been given dataset access privileges. When a dataset has been shared with another user via the sharing control panel, BigQuery sends a notification email containing a direct link to the dataset.
The BigQuery UI features a collection of public datasets for you to use when trying out these new features. To get started, visit our sign up page and Quick Start guide. You should take a look at our API docs, and ask questions about BigQuery development on Stack Overflow. Finally, don't forget to give us feedback and join the discussion on our Cloud Platform Developers Google+ page.
Michael Manoochehri is a Developer Programs Engineer supporting the Google Cloud Platform. His goal is to help make cloud computing and data analysis universally accessible and useful.
Posted by Scott Knaster, Editor
2013, By: Seo Master
By Michael Manoochehri, Developer Programs Engineer, Cloud Platform
Google BigQuery is designed to make it easy to analyze large amounts of data quickly. Overwhelmingly, developers have asked us for features to help simplify their work even further. Today we are launching a collection of updates that gives BigQuery a greater range of query and data types, more flexibility with table structure, and better tools for collaborative analysis.
Big JOIN and Big Group Aggregations
Extracting insights from multiple datasets can be challenging and time-consuming. This is especially true when datasets become too large to query with a traditional database system. With traditional databases, SQL functions like joining and grouping are typically used to bring together data for analysis. What happens when your data is too large to fit into a conventional database? Working with multi-terabyte datasets often requires developing complicated MapReduce workflows, investing in expensive infrastructure, and great deal of time. Very often, it's a combination of all three.
In response to developer feedback, we're launching new features that enable analysts and developers to run fast SQL-like join and aggregate queries on datasets without the need for batch-based processing. Our new Big JOIN feature gives users the ability to produce a result set by merging data from two large tables by a common key. Big JOIN simplifies data analysis that would otherwise require a data transformation step, by allowing users to specify JOIN operations using SQL.
Popular web applications produce user activity logs that can grow by billions of rows each week. Dividing users into smaller groups is a key step for analysis. However, each group of users can number in the millions. To handle this for such large volumes, we've enabled Big Group Aggregations, which significantly increases the number of distinct values that can be grouped in a result set.
To use these new features, simply add the EACH modifier to JOIN or GROUP BY clauses.
| /* JOIN EACH example * Selects the top 10 most edited Wikipedia pages * of words that appear in works of Shakespeare. */ SELECT TOP(wiki.title, 10), COUNT(*) FROM [publicdata:samples.wikipedia] AS wiki JOIN EACH [publicdata:samples.shakespeare] AS shakespeare ON shakespeare.word = wiki.title; |
For more information, including best practices, when using JOIN EACH and GROUP EACH BY, visit the BigQuery query reference.
Native support for TIMESTAMP data type
We are also adding a new TIMESTAMP data type, in response to one of our most frequent requests from developers. This new data type lets you import date and time values in formats familiar to users of databases such as MySQL, while preserving timezone offset information.
Along with the new data type come new functions for converting TIMESTAMP fields into other formats, calculating intervals, and extracting components such as the hour, day of week, and quarter.
| /* TIMESTAMP example * Which hours in the day are the most popular for GitHub actions? * This query converts github_timeline "created_at" date time * strings to BigQuery TIMESTAMP, and extracts the hour from each. */ SELECT HOUR(TIMESTAMP(created_at)) AS event_create_hour, COUNT(*) AS event_count FROM [publicdata:samples.github_timeline] GROUP BY event_create_hour ORDER BY event_count DESC; |
Read more about the available TIMESTAMP functions in our query reference guide.
Add columns to existing BigQuery tables
When working with large amounts of fast moving data, it's not uncommon to find out that you need to add additional fields to your tables. In response to developer feedback, we have added the ability to add new columns to existing BigQuery tables.
To take advantage of this feature, simply provide a new schema with additional columns using either the "Tables: update" or "Tables: patch" BigQuery API methods.
For more information on this feature, visit the BigQuery API reference.
BigQuery Web UI: Dataset links and dataset sharing notifications
BigQuery has always provided project owners with very fine-grained control of how their datasets are shared. To make it easier for teams to work on collaborative data analysis, we've added direct links to individual datasets in the BigQuery Web UI. This provides a convenient way for authorized users to quickly access a dataset, and allows for bookmarking and sharing.
In addition, we've also added email notifications to inform users when they've been given dataset access privileges. When a dataset has been shared with another user via the sharing control panel, BigQuery sends a notification email containing a direct link to the dataset.
The BigQuery UI features a collection of public datasets for you to use when trying out these new features. To get started, visit our sign up page and Quick Start guide. You should take a look at our API docs, and ask questions about BigQuery development on Stack Overflow. Finally, don't forget to give us feedback and join the discussion on our Cloud Platform Developers Google+ page.
Michael Manoochehri is a Developer Programs Engineer supporting the Google Cloud Platform. His goal is to help make cloud computing and data analysis universally accessible and useful.
Posted by Scott Knaster, Editor
2013, By: Seo Master
Seo Master present to you:
There are many mistakes organizations and individuals can make which can lead to theft of their data. Taking simple measures can certainly safeguard your data from potential theft. The following is a list of things you need to be aware of in-order to protect yourself.
Shred your paper work:
If you decide to discard any documents, such as credit statements, bank statements, utility bills and other related information, then you need to make it a habit to shred such documents every time you decide to throw them out. More than often, Dumpster-divers will often go through your pile of garbage seeking out un-shredded statements containing bits of your personal information. Once these so called “Dumpster-divers” get a hold of your information, it’s only a matter of time before they use your information to max out your credit card, or perhaps transfer your life savings from your savings account, to an offshore account.
To effectively shred such documents, make sure you make use a cross-shredder instead of a traditional shredder. Cross-shredders can shred such documents much more effectively than traditional shredders.
Smash your old hard-drive into pieces:
This step may seem a bit on the extreme side, but that’s what you have to do in order to keep your personal data safe. Even if you had previously stored personal data on your hard-drive and then happened to delete that data -- thinking it was permanently wiped-out of your hard-drive; chances are your deleted data could still be retrieved. Data recovery software can easily recover most permanently deleted data.
When you empty your recycle bin, it may appear that the data stored in your recycle bin is gone forever. However, bits of data are always present on your hard-drive, and basic data recovery software can easily retrieve such deleted data. Thus, when you send in your old desktop or laptop for recycling, chances are that someone may try to retrieve your personal data on your laptop or desktop. Therefore, this is the reason why you need to smash your hard-drive, before you recycle your machine.
Use anti-data theft software and antivirus software:
If you, for some reason, need to store confidential data on your personal computer, you can do so securely with the help software that can lock folders. Moreover, investing in good anti-virus software will help you keep your identity safe on the internet.
Stay away from Phishing email:
Phishing is the practice of recording your personal user-names and passwords so that some kind of fraud can be conducted using your log-in credentials. The scam works something like this: you receive an email from Facebook, or perhaps your bank, prompting you to log-in, so that your identity can be confirmed. Most people don’t think twice about such emails, and will log-in to confirm their accounts and to prove their identities. Little do you know, in the background, your credentials are being recorded by key-logging software!
Follow my blog with Bloglovin2013, By: Seo Master
Theft of personal data is a rising phenomenon and seems to be growing as technology further progresses. What’s more, businesses and organizations are equally affected by this menace. Despite having in place data security measures, individuals and organizations can still fall victim to data thieves. So why is your data so important to these e-criminals? The answer is simple; your data can be used clear-out your bank account, or worse, steal your identity to commit fraud and other criminal activities.
There are many mistakes organizations and individuals can make which can lead to theft of their data. Taking simple measures can certainly safeguard your data from potential theft. The following is a list of things you need to be aware of in-order to protect yourself.
Shred your paper work:
If you decide to discard any documents, such as credit statements, bank statements, utility bills and other related information, then you need to make it a habit to shred such documents every time you decide to throw them out. More than often, Dumpster-divers will often go through your pile of garbage seeking out un-shredded statements containing bits of your personal information. Once these so called “Dumpster-divers” get a hold of your information, it’s only a matter of time before they use your information to max out your credit card, or perhaps transfer your life savings from your savings account, to an offshore account.
To effectively shred such documents, make sure you make use a cross-shredder instead of a traditional shredder. Cross-shredders can shred such documents much more effectively than traditional shredders.
Smash your old hard-drive into pieces:
This step may seem a bit on the extreme side, but that’s what you have to do in order to keep your personal data safe. Even if you had previously stored personal data on your hard-drive and then happened to delete that data -- thinking it was permanently wiped-out of your hard-drive; chances are your deleted data could still be retrieved. Data recovery software can easily recover most permanently deleted data.
When you empty your recycle bin, it may appear that the data stored in your recycle bin is gone forever. However, bits of data are always present on your hard-drive, and basic data recovery software can easily retrieve such deleted data. Thus, when you send in your old desktop or laptop for recycling, chances are that someone may try to retrieve your personal data on your laptop or desktop. Therefore, this is the reason why you need to smash your hard-drive, before you recycle your machine.
Use anti-data theft software and antivirus software:
If you, for some reason, need to store confidential data on your personal computer, you can do so securely with the help software that can lock folders. Moreover, investing in good anti-virus software will help you keep your identity safe on the internet.
Stay away from Phishing email:
Phishing is the practice of recording your personal user-names and passwords so that some kind of fraud can be conducted using your log-in credentials. The scam works something like this: you receive an email from Facebook, or perhaps your bank, prompting you to log-in, so that your identity can be confirmed. Most people don’t think twice about such emails, and will log-in to confirm their accounts and to prove their identities. Little do you know, in the background, your credentials are being recorded by key-logging software!
Seo Master present to you:
- Trend to inside the minds of searchers
Often we as marketers think we know a lot about how people search. The truth is, there are a lot of different ways to search and it varies by industry and from one individual to another. By analyzing the keywords and phrases that are driving traffic and sales to your website, you can find out how your customers search to find your site. What adjectives or other modifiers do potential customers search on? What order do they search?
- Which keyword are working for organic search
If your site is showing up on the first page for some of those keywords, how much traffic are you getting from those organic listings? More importantly, how many leads or sales are you getting from those keywords? You will sometimes be surprised at which keywords drive the most traffic. Often it's not the keywords you think will be best, and that's why you have to watch your keyword referral reports to see which keywords are working.
- Calculate which keywords are not driving users
If you're on the first page of Google and you get zero clicks, it's time to find some new keywords. Stick with the keywords that drive sales and ditch the keywords that don't work. There is a huge difference in click through rates depending on the position your site is listed in, but if your site is anywhere on the first page of Google, you should expect some level of traffic, or you're not targeting the right keyword.
- Searching keywords which works in PPC that can be used for SEO
The nice thing about PPC search advertising is that you can choose exactly which keywords your ad shows up for. The thing that sucks about PPC is that you have to pay for every click. So why not take what you've learned from your PPC campaign and make sure you're focusing your SEO efforts on the right keywords? You'll usually find that a first page organic listing for the same keyword will send a lot more traffic than a paid listing for the same phrase, and the price per click is way better.
- Searching keywords which works for SEO that can be used for PPC lunching
The same idea for taking PPC keywords into your SEO campaign works the other way, too. Organic search listings will bring people to your site for all kinds of different keywords–including tons of keyword combinations that you never would have thought to include in your PPC campaign. If you notice a particular phrase that drives a lot of sales from a unique organic search keyword, you should try it out in your PPC ads. You'll usually see a similar conversion rate, or maybe even better conversion from PPC on the same keyword!
- Select keywords to insert as negative matches
Negative matching with PPC campaigns is when you tell the search engines to not show your ad when certain words are included in the search query. This can come in handy when you're doing broad matching on keywords that have multiple meanings or connotations. They can also help you eliminate keywords that are driving a lot of traffic without resulting in sales. By watching your conversion metrics on a keyword level, you can identify keywords that drive traffic without sales and add those keywords to your campaigns as negative matches. You can even save yourself some money by looking at irrelevant, under-performing keywords from your organic search that should be excluded from your PPC campaigns before you even spend a penny on PPC ads.
- More ideas to get for new content and products
You'll start to notice that people find your site for all kinds of different, sometimes strange, keywords. Watch the keyword list for new ideas for topics you can write about on your blog or even a new product you can add to meet the needs of your customers. If you're getting significant traffic on keywords that you don't have content about, it's a good indicator that traffic would flow to your site if you create content to match what people are looking for.
2013, By: Seo Master