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macOS 11 Big Sur has just been announced at WWDC 2020, and it brings some pretty major improvements to the operating system behind the best Macs, along with some stability improvements.

macOS Big Sur follows macOS Catalina, but where Catalina only had minor improvements over macOS Mojave, this new version is the biggest change to Apple's operating system in years.

Chief among these is the migration to Apple-designed silicon. This has been rumored for years, but this, along with macOS Catalyst, will finally bring support for every iOS and iPad app to the Mac operating system. Apple also promises that it will lead to greater efficiency and power – though that remains to be seen.

We also get a massive redesign in the look of native macOS apps, with Apple giving apps like Messages, Mail, Photos, Calendar and even Finder a fresh, much more compact and streamlined design.

Also, we're finally getting improvements that are more in line with what you get on the iOS, with macOS Big Sur bringing that new widgets feature we're getting with iOS 14 and iPadOS 14, making your whole Apple experience a much more harmonious and unified experience.

We didn't get an actual release date for macOS 11, but if Apple follows its typical release schedule, as we're pretty sure it will, we should see the next Mac operating system hit our computers in either September or October 2020. Still, if you want to get your hands on the operating system right now, you can jump into the developer's beta starting today, though you should keep in mind that it's not free.

This is the biggest macOS release in years, so there's a lot to talk about. Be sure to keep this page bookmarked, and we'll keep it updated with the latest information and features.

Cut to the chase

• What is it? macOS 11 Big Sur, successor to macOS 10.15 Catalina
• When is it out? Likely September or October 2020
• How much will it cost? Nothing. Apple software updates are always free

macOS 11 Big Sur release date

While macOS Big Sur was revealed today, we don't know exactly when the general public will be able to download and install it.

Typically Apple releases its software at the same time each year, so it's reasonable to expect the macOS 10.16 release date to fall somewhere in September or October 2020. Either way, we won't actually know the exact date the software will be publicly available until the iPhone 12 event later this year.

Still, if you're eager to get your hands on the software, the beta version is available today if you're a part of the Apple Developer program, which will cost you $99 (about £79, AU$140). We must urge caution to most folks here, though. Early versions of software are prone to bugs, and aren't quite as secure as public releases. If you're ok with the risks, though, the option is open to you.

macOS 11 Big Sur system requirements

Gallery: The best Chromebook 2020: Our pick of the top Chrome OS laptops (Pocket-lint)

If you want to download and install macOS 11 when it becomes publicly available later this year, you're going to want to make sure your Mac is actually able to run it. And, unfortunately macOS system requirements have gone up.

We went ahead and listed the macOS Big Sur-compatible Mac systems down below.

• 12-inch MacBook (2015 and later)
• MacBook Air (2013 and later)
• MacBook Pro (Late 2013 and later)
• Mac mini (2014 and later)
• iMac (2014 and later)
• iMac Pro (all models)
• Mac Pro (2013 and later)

macOS 11 Big Sur name

This time around, Apple chose Big Sur to symbolize this release of macOS. Much like the unincorporated coastal area in Northern California, this new macOS is supposed to deliver 'unmatched levels of power and beauty.'

macOS 11 Big Sur features

Safari

This new version of Safari will bring a host of new features to the table including Intelligent Tracking that can give you a Privacy Report on each website you visit, Save Passwords to track your passwords and make sure they haven't been compromised, Extensions support for WebExtensions API and a new Extensions category in the App Store, and native translation capabilities.

One cool thing here is that the Home Page will now be extremely customizable so you can change the background image and add/edit sections.

Messages improvements

Messages on macOS has been behind iOS for a while (and kind of still is), but now you can use Pinned Messages, Memoji and the Groups Enhancements that will come with the iOS 14, which is a nice touch. Basically, Messages for Mac will also bring many of the features that its iOS 14 and iPad OS 14 versions will have. It'll also feature a more powerful search, a redesigned photo picker and new messages special effects.

AirPods improvements

Sidebar in Mail and Photos

Control Center on Mac

Widgets in the notifications app

Mac Catalyst

One of the biggest headline features of macOS Mojave was that it brought some big-name iOS apps to the Mac. However, through Mac Catalyst, new APIs and tools will help app developers bring more iOS apps over to the Mac operating system. Through these tools, you'll get features like resizable windows and keyboard tools, which will make them feel like Mac Apps, rather than iPhone apps.

Mac on ARM

After so many rumors, it's finally happened. Apple has finally announced that Macs will be transitioning to Apple-designed silicon, similar to what we've seen with every other device in its lineup.

However, this isn't quite the death knell for Intel that some might think it is. Tim Cook said during the WWDC keynote that there are several Intel-based devices still in the works at Apple, and that the Cupertino behemoth is 'very excited' about them.

Still, this is probably the biggest change to come to the best Macs in years, as it will natively allow all iPhone and iPad apps to work natively on Macs for the first time. Plus, thanks to Mac Catalyst, and Rosetta 2, which will translate the source code of all Mac Apps, every Mac App will be able to run on the new ARM-based Macs that launch later this year.

Apple didn't announce any specific Mac devices that will be using this hardware, but hinted that they're coming – plus we got to see an unnamed iMac running the new software. Still, Developers can apply for Apple's Universal App Quick Start Program, to get access to a Mac Mini running the Apple A12Z Bionic SoC. However, this program will cost developers $500 (about £400 / AU$720).

• These are the best Macs of 2020

Unitale mac os. The Hadoop ecosystem today is very rich and growing. A technology that I use and enjoy quite a bit in that ecosystem is Hive. From the Hive wiki, Hive is 'designed to enable easy data summarization, ad-hoc querying and analysis of large volumes of data'. To add to that statement, Hive is also an abstraction built on top of Map Reduce that lets you express data processing using a SQL-like syntax described in detail here. Hive reduces the need to deeply understand the Map Reduce paradigm and allows developers and analysts to apply existing knowledge of SQL to big data processing. It also makes expressing Map Reduce jobs more declarative.

One thing I do hear a lot from folks is that Hive, being schema driven and having typed columns, is only fit for processing structured and row oriented tabular data. Although this seems like a logical conclusion, it is very good at processing unstructured data into a structured form too. Not only that, it puts structure around processing of unstructured data that has higher level of abstraction than Map Reduce.

So before we get into the details of processing unstructured data in Hive, I'll mention some other features and concepts of Hive.

Hive Meta Store

Hive uses a meta store to store meta data about the data, usually MySQL is used in production. The meta store stores the table meta data like table names, columns and types, etc.

User Defined Functions

Hive provides many User Defined Functions (UDF) out of the box and makes it really easy to write custom ones. There are three main kinds of UDF's in Hive.

1. Generic UDFs are used for operating on a single column value. For example lower() is a UDF that will lower case a string value.
2. User Defined Aggregate Functions (UDAF) are used when aggregating on a value or set of values grouped by some columns. For example, sum() which will return the sum of a column with or without a group by clause.
3. User Defined Table-Generating Functions (UDTF) are used when you want to generate rows from a column. For example, explode() takes a array or map as input and returns multiple rows.

Tables and Partitions

Hive supports two main kinds of tables: external and non external. With external tables, the data is added to the table by using a load partition command. For non external tables, the data goes in whichever folder you specified in LOCATION block of the create statement. If no LOCATION is specified, hive will use its default base location specified in its configuration variable named ' hive.metastore.warehouse.dir'. When you drop an external table, the data is not deleted. But, when you drop a non external table, the data is deleted along with the table. You can think of the data in Hive tables like giant CSV's with some pre-determined delimiter defined when creating the table.

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Since big data can be, well, big, it is not always optimal to scan the entire folder or table in Hive. For example, if you are interested in data for a specific date range, then limiting the data to that date range and then processing can be much more efficient than a full table scan. For this reason Hive supports partitions. Partition columns are virtual columns, they are not part of the data itself but are derived on load. Partitions columns don't have to be dates, but many times, at least one of the columns tends to be a date type.

Here is how to create a partitioned external table where rows are delimited by comma:

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Processing Un Structured Data Using Hive

One of the popular use cases for Hadoop is processing large volumes of unstructured logs. I'll use this as an example to illustrate using Hive to parse unstructured data and store in a query-able structured form. As an example, here are a few lines of what a typical access log file might look like:

So using the table named 'access_log' defined in the create statement example, we can load this data in that table. Let's assume the access log data is stored in the following HDFS location:

/user/demo/access_logs/2014/07/04/00/00

Running the following alter table statements, which takes advantage of the partitioned feature of the table, will load the data into a new partition on the table :

Next, setup the table to store the parsed log data into: The dream carnival 2020 mac os.

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Once the raw table and the output tables are in place, script the parsing of the data using Hive SQL and taking advantage of Hive's many built-in string parsing UDF's. Again, make sure to add the destination and partition for the the parsed data:

Finally, parse the data using Hive SQL with UDF's:

Once the load query completes, you can select * the processed structured version of the log data from the 'parsed_access_log' table to get following tabular structured results:

So there you have it, Hive can be used to effectively process unstructured data. For the more complex processing needs you may revert to writing some custom UDF's instead. There are many benefits to using higher level of abstraction than writing low level Map Reduce code. One of the benefits that makes Hive appealing to me is the lack of boiler plate code for Mapper, Reducers, and Drivers. The biggest benefit is the declarative SQL-like syntax. It's easier to follow and it's a good fit for developer and non-developer folks to take full advantage of Big Data using an existing skill set.