The source code The slides for the talk "Mobile First!" is the new cry of web designers worldwide. But how do you do it? Do you have to scrap all of your current web skills? Is it magic created by wizard-like designers which could never be understood by mere mortals? Believe it or not, with the combination of jQuery Mobile and CSS3 Media Queries, you can easily create a site that looks good on a phone, tablet, or desktop. Does Mobile-First Still Matter? Yes! You still need to look to design all features of your programs with a mobile-first mentality. People are spending increasing amounts of time on their phones, and they expect the companies that create products for them to come up with something that will enable them to continue to view those products from their phones. If you are unable to do this for them, they may simply move on to another company that can provide them with that kind of functionality. You have a lot of choices to make when it comes to the specific features that you will use and embrace, but you need to consider how going mobile first can help bolster the chances of your application being used by the masses. General Responsive Web Features The web began as responsive. Now admittedly, the web didn't do very much, so being responsive when the Internet was mainly documents was easy. HTML documents naturally wrapped to the next line and flowed down the page. Along the way, things changed. Developers began designing sites in tools like PhotoShop and wanted perfect pixel renderings of those designs. The problem with pixels is that they are not very flexible. It has always been possible to use percentages instead of pixels, but they were clumsier to work with, so pixels remained the favorite. With HTML5 and CSS3 there is more support for responsive design. Lets Meta Tags Meta tags have been the favorite of the SEO crowd for some time. Meta tags are used to define keywords, descriptions, and even redirects. Here are some rules about meta tags: They always go in the section of the page They are never displayed They consist mostly of key/value pairs: name = key and content = value Viewport The viewport is a special type of meta tag which defines the screen of a mobile device. In the example program the viewport meta tag looks like: The attributes above mean: width=device-width - converts the pixels to CSS pixels initial-scale=1 - sets the scale level user-scalable=no - turns off scaling If the initial scale is some other value than 1, the zoom can be smaller or larger. If user-scalable is set to yes, then the user sets the zoom level by tapping, pinching, or whatever zoom gesture the browser supports. Media Query Media Queries are the workhorse of responsive design. A media query is a media type and at least one expression that limits the style sheets' scope. Here is an example: @media screen and (max-width: 1024px) and (orientation:portrait) { } The above media query means: For a screen media type Define the classes only if The width is less than 1023 AND the orientation is portrait The media query begins with @media then a media type, in this case, screen. Other types are all, braille, embossed, handheld, print, project, speech, tty, and tv. You can compose complex media queries using logical operators like not, and, and only. not - is used to negate an entire media query and - used to combine multiple media features together into a single media query only - used to apply a style only if the entire query matches Finally, there is the comma-separated list which behaves like an operator. If any media queries return true, the style sheets get applied. One pattern for applying the media queries is to define the query for the narrowest device first, then define it for a tablet, and finally a desktop. Now all of these definitions are pretty loose and open to interpretation. You may need to adjust them to fit your needs. If you run the demo on a high pixel phone like a Nexus 4, which has a display of 1280x768 resolution, why doesn't it display like a desktop? The key is the viewport meta tag. This tag, which is read by mobile browsers, redefines the pixels as CSS pixels. The precise number of CSS pixels varies by device, but on the iPhone, it is 320 and on the Nexus 4 it is 384, both of which are less than the minimum of 480 pixels to be defined as a tablet. jQuery Mobile Features So far we haven't looked at jQuery Mobile features. From the get go jQuery Mobile has had responsive features. Some of which are: grid - a simple way to build CSS-based columns that can also be responsive tables - selectively hide or shows table columns based on the display width panels - create a hidden page that slides left or right to reveal itself Grids Grids have been with jQuery Mobile since the beginning. They are essentially self sizing columns that dynamically resize themselves when the size of the page changes. The number of available columns ranges from two to five. To change the number of available columns simply change the class on the root p then add or remove a p from the collection. ui-grid-a = 2 columns ui-grid-b = 3 columns ui-grid-c = 4 columns ui-grid-d = 5 columns Tables Tables were added with the release of jQuery Mobile 1.3.0. They allow for the responsive display of tabular data. There are two basic types of tables: reflow which is the default and column toggle. Reflow tables lay the table data horizontally until it reaches a minimum size, then all of the data for each row is grouped together and it re-flows down the page. In column toggle mode, each column of a table can be given a separate priority, when the data can no longer fit horizontally, the column with the lowest priority number which is still visible is hidden. This continues until a minimum size is reached or there is only one column remaining. Panels A panel is a hidden page that reveals itself by sliding from the left or right onto the page. It can support nearly any jQuery Mobile widget. When the panel is displayed, clicking anywhere else on the page will close it. Best Practices Design styles beginning with "mobile first", then go wider Use "min-width" to constrain styles Prefer percentages and ems to pixels
I wrote about cypher’s MERGE function a couple of weeks ago, and over the last few days, I’ve been exploring how it works with schema indexes and unique constraints. An Exciting Time to Be a Developer There is so much that could be said about the merging of Neo4j and Cypher right now, but it is certainly reasonable to point out that this merger will likely result in many exciting developments in the programming world. Programmers virtually always appreciate it when they are given the products and tools they require to get their job done properly, and now is the time for steps like this to be taken. The fact that Neo4J and Cypher have decided to merge means that the upsides of both will soon be apparent. You deserve to use all of the best tools to make informed decisions about your next software project, and a great way to make it happen is to use what has been given to you regarding product functionality. This is to say that you can use both the upsides of Neo4J and Cypher to come up with the exact tools you need to make a difference in your sphere of influence. Could Other Products Soon Merge? There has been some strong demand for other software development products to consider merging. Coders and programmers want to use their favorite projects in exactly how they were meant to be used, and this means getting them to merge in ways that are useful to the programmers. They just want to be able to squeeze as much use out of each program as they possibly can. You want to make sure that you can see what is going on with your codes as you are directly applying them to whichever problem you are working on at this time. To be sure, it is not an easy task, but no one ever said it would be easy. The important thing is that you get the work done so that you can start to become more productive in the coding you are doing now. A common use case with Neo4j is to model users and events where an event could be a tweet, Facebook post, or Pinterest pin. The model might look like this: We’d like to ensure that we don’t get duplicate users or events, and MERGE provides the semantics to do this: MERGE (u:User {id: {userId}) MERGE (e:Event {id: {eventId}) MERGE (u)-[:CREATED_EVENT]->(m) RETURN u, e We’d like to ensure that we don’t get duplicate users or events and MERGE provides the semantics to do this: MERGE ensures that a pattern exists in the graph. Either the pattern already exists, or it needs to be created. import org.neo4j.cypher.javacompat.ExecutionEngine; import org.neo4j.cypher.javacompat.ExecutionResult; import org.neo4j.graphdb.GraphDatabaseService; import org.neo4j.graphdb.factory.GraphDatabaseFactory; import org.neo4j.helpers.collection.MapUtil; import org.neo4j.kernel.impl.util.FileUtils; ... public class MergeTime { public static void main(String[] args) throws Exception { String pathToDb = "/tmp/foo"; FileUtils.deleteRecursively(new File(pathToDb)); GraphDatabaseService db = new GraphDatabaseFactory().newEmbeddedDatabase( pathToDb ); final ExecutionEngine engine = new ExecutionEngine( db ); ExecutorService executor = Executors.newFixedThreadPool( 50 ); final Random random = new Random(); final int numberOfUsers = 10; final int numberOfEvents = 50; int iterations = 100; final List userIds = generateIds( numberOfUsers ); final List eventIds = generateIds( numberOfEvents ); List merges = new ArrayList<>( ); for ( int i = 0; i < iterations; i++ ) { Integer userId = userIds.get(random.nextInt(numberOfUsers)); Integer eventId = eventIds.get(random.nextInt(numberOfEvents)); merges.add(executor.submit(mergeAway( engine, userId, eventId) )); } for ( Future merge : merges ) { merge.get(); } executor.shutdown(); ExecutionResult userResult = engine.execute("MATCH (u:User) RETURN u.id as userId, COUNT(u) AS count ORDER BY userId"); System.out.println(userResult.dumpToString()); } private static Runnable mergeAway(final ExecutionEngine engine, final Integer userId, final Integer eventId) { return new Runnable() { @Override public void run() { try { ExecutionResult result = engine.execute( "MERGE (u:User {id: {userId})\n" + "MERGE (e:Event {id: {eventId})\n" + "MERGE (u)-[:CREATED_EVENT]->(m)\n" + "RETURN u, e", MapUtil.map( "userId", userId, "eventId", eventId) ); // throw away for ( Map row : result ) { } } catch ( Exception e ) { e.printStackTrace(); } } }; } private static List generateIds( int amount ) { List ids = new ArrayList<>(); for ( int i = 1; i <= amount; i++ ) { ids.add( i ); } return ids; } } We create a maximum of 10 users and 50 events and then do 100 iterations of random (user, event) pairs with 50 concurrent threads. Afterward, we execute a query that checks how many users of each id have been created and get the following output: +----------------+ | userId | count | +----------------+ | 1 | 6 | | 2 | 3 | | 3 | 4 | | 4 | 8 | | 5 | 9 | | 6 | 7 | | 7 | 5 | | 8 | 3 | | 9 | 3 | | 10 | 2 | +----------------+ 10 rows Next, I added a schema index on users and events to see if that would make any difference, something Javad Karabi recently asked on the user group. CREATE INDEX ON :User(id) CREATE INDEX ON :Event(id) We wouldn’t expect this to make a difference as schema indexes don’t ensure uniqueness, but I ran it anyway t and got the following output: +----------------+ | userId | count | +----------------+ | 1 | 2 | | 2 | 9 | | 3 | 7 | | 4 | 2 | | 5 | 3 | | 6 | 7 | | 7 | 7 | | 8 | 6 | | 9 | 5 | | 10 | 3 | +----------------+ 10 rows If we want to ensure the uniqueness of users and events, we need to add a unique constraint on the id of both of these labels: CREATE CONSTRAINT ON (user:User) ASSERT user.id IS UNIQUE CREATE CONSTRAINT ON (event:Event) ASSERT event.id IS UNIQUE Now if we run the test, we’ll only end up with one of each user: +----------------+ | userId | count | +----------------+ | 1 | 1 | | 2 | 1 | | 3 | 1 | | 4 | 1 | | 5 | 1 | | 6 | 1 | | 7 | 1 | | 8 | 1 | | 9 | 1 | | 10 | 1 | +----------------+ 10 rows We’d see the same result if we ran a similar query checking for the uniqueness of events. As far as I can tell, this duplication of nodes that we merge on only happens if you try to create the same node twice concurrently. Once the node has been created, we can use MERGE with a non-unique index, and a duplicate node won’t get created. All the code from this post is available as a gist if you want to play around with it.
The author conducts two tests with differing service delay times to measure any difference in performance between reactive and synchronous programming.
Find a suitable client tool for MQTT testing. Listed by desktop, browser, and command line categories, these tools are available for free (most, open source).
You have probably heard a lot of talk about the wonderful things the cloud can do for you, and you are probably curious about how those services may come into play in your daily life. If this sounds like you, then you need to know that cloud services are playing an increasingly important role in our lives, and we need to look at how they can change how we message one another. Many people are looking at Android cloud messaging as the next leap forward into a future where it is possible to reach out to the people we care about and save those messages directly in the cloud. Never miss the opportunity to communicate with someone who truly matters to you, and start using cloud storage to back up your messages. It is as simple as that! You might have heard of c2dm (cloud-to-device messaging), which basically allowed third-party applications to send (push) lightweight messages to their android applications. Well, c2dm as such is now deprecated and replaced with its successor up the evolutionary ladder: GCM, or google cloud messaging. GCM is a (free) service that allows developers to push two types of messages from their application servers to any number of android devices registered with the service: collapsible, "send-to-sync" messages non-collapsible messages with a payload up to 4k in size "Collapsible" means that the most recent message overwrites the previous one. A "send-to-sync" message is used to notify a mobile application to sync its data with the server. In case the device comes online after being offline for a while, the client will only get the most recent server message. If you want to add push notifications to your android applications, the getting started guide will walk you through the setup process step by step, even supplying you with a two-part demo application (client + server) that you can just install and play around with. The setup process will provide you with the two most essential pieces of information needed to run GCM: An API Key is needed by your server to send GCM push notifications A Sender ID is needed by your clients to receive GCM messages from the server Everything is summarized in the following screen you get after using the google API console: The quickest way to write both server and client code is to install the sample demo application and tweak it to your needs. In particular, you might want to at least do any of the following: Change the demo's in-memory datastore into a real persistent one. Change the type and/or the content of push messages. Change the client's automatic device registration on start-up to a user preference so that the handset user may have the option to register/unregister for the push notifications. We'll do the last option as an example. Picking up where the demo ends, here's a quick way to set up push preferences and integrate them into your existing android application clients. in your android project-resources ( res/xml) directory, create a preference.xml file such as this one: and the corresponding activity: // package here import android.os.bundle; import android.preference.preferenceactivity; public class pushprefsactivity extends preferenceactivity { @override protected void oncreate(bundle savedinstancestate) { super.oncreate(savedinstancestate); addpreferencesfromresource(r.xml.preferences); } } the above will provide the following ui: The "enable server push" checkbox is where your android application user decides to register for your push messages. Then, it's only a matter of using that preferences class in your main activity and doing the required input processing. the following skeleton class only shows your own code add-ons to the pre-existing sample application: // package here import com.google.android.gcm.gcmregistrar; // other imports here public class mainactivity extends activity { /** these two should be static imports from a utilities class*/ public static string server_url; public static string sender_id; private boolean push_enabled; /** called when the activity is first created. */ @override public void oncreate(bundle savedinstancestate) { super.oncreate(savedinstancestate); // other code here... processpush(); } /** check push on back button * if pushprefsactivity is next activity on stack */ @override public void onresume(){ super.onresume(); processpush(); } /** * enable user to register/unregister for push notifications * 1. register user if all fields in prefs are filled and flag is set * 2. un-register if flag is un-set and user is registered * */ private void processpush(){ if( checkpushprefs() && push_enabled ){ // register for gcm using the sample app code } if(! push_enabled && gcmregistrar.isregisteredonserver(this) ){ gcmregistrar.unregister(this); } } /** check server push preferences */ private boolean checkpushprefs(){ sharedpreferences prefs = preferencemanager .getdefaultsharedpreferences(this); string name = prefs.getstring("sname", ""); string ip = prefs.getstring("sip", ""); string port = prefs.getstring("sport", ""); string senderid = prefs.getstring("sid", ""); push_enabled = prefs.getboolean("enable", false); boolean allfilled = checkallfilled(name, ip, port, senderid); if( allfilled ){ sender_id = senderid; server_url = "http://" + ip + ":" + port + "/" + name; } return allfilled; } /** checks if any number of string fields are filled */ private boolean checkallfilled(string... fields){ for (string field:fields){ if(field == null || field.length() == 0){ return false; } } return true; } } The above is pretty much self-explanatory. Now GCM push notifications have been integrated into your existing application. If you are registered, you get a system notification message at each server push, even when your application is not running. Opening up the message will automatically open your application: GCM is pretty easy to set up since most of the plumbing work is done for you. a side note: if you like to isolate the push functionality in its own sub-package, be aware that the GCM service gcmintentservice, provided by the sample application and responsible for handling GCM messages, needs to be in your main package (as indicated in the-set up documentation)—otherwise GCM won't work. When communicating with the sample server via an HTTP post, the sample client does a number of automatic retries using exponential back-off, meaning that the waiting period before a retry in case of failure is each time twice the amount of the preceding wait period, up to the maximum number of retries (5 at the time of this writing). You might want to change that if it doesn't suit you. It may not matter that much, though, since those retries are done in a separate thread (using asynctask) from the main UI thread, which therefore minimizes the effects on your mobile application's pre-existing flow of operations.
For developers having worked on J2EE web applications for many years, getting into Flex will seem both very fun and familiar of the simplicity and power of ActionScript and the UI framework, and quite tedious and frustrating when it comes to developing the core application logic and the server integration. In some ways, developing Flex applications with widely used frameworks like Cairngorm and BlazeDS involves a lot of plumbing code (business delegates, service facades, conversions between JPA entities and value objects,...) and will remind you of the days of Struts and EJB2. Data is King Let’s not pretend like data isn’t and won’t continue to be a nearly invaluable tool that companies and individuals alike can use to make a lot of progress towards their end goals. If we look at things objectively, it is clearly possible that data is the most valuable resource known to man at this time. It seems like a good idea to both invest in data services and to build up one’s knowledge about what they are and why they are so important to the development of many projects at this time. If we can all learn a little more about this topic, then we stand a very good chance of being able to use data in the ways that it was designed to be used. The Granite Data Services project was started with the (ambitious) goal of providing Flex with the same kind of development model we were used to with modern J2EE frameworks. The GDS remoting functionality has been designed from the beginning to support the serialization of JPA/Hibernate detached entities and to easily connect to the most important J2EE frameworks (EJB3, Spring, Seam, Guice). In most cases, this removes the need to write and maintain service facades and value objects on the J2EE layer. In fact, that finally means that a Flex client can consume the exact same set of server services as a classic web application. Another repetitive task is to build the ActionScript model classes. GraniteDS provides the Gas3 tool that can automatically generate the ActionScript model classes from the Java data model. With the latest GraniteDS 1.1 release, the process is still improved by the Granite Eclipse builder plugin, which regenerates on the fly the necessary ActionScript classes whenever JPA entities are created or modified in the Eclipse project. You just have to write your JPA data model, and you can directly make use of the generated AS3 classes in the Flex UI layer. This is already a big step towards a more simple server integration in Flex, but GDS 1.1 brings an even simpler programming model. It targets only JBoss Seam for now but integration with Spring and EJB3 are already on the way. The Tide project aims at providing the same simplicity to build Flex/AIR applications that JBoss Seam has brought to J2EE. Tide requires almost no configuration during development and automates most of the plumbing code generally found for example in Cairngorm business delegates or service locators. Contrary to other Flex frameworks whose goal is to put all business logic on the client, Tide client/server interactions are exclusively done by method calls on exposed services, allowing to respect transaction boundaries, security, and validation rules defined by these services. Tide mainly consists of a Flex library that provides data-oriented functionality : An entity cache ensures that all managed entity instances are unique in a Tide context. This allows in particular to maintain correct data bindings between calls to remote services. A collection wrapping mechanism that enables transparent initialization of lazy collections. A Flex collection component integrated with JBoss Seam’s paged query component that can be used as a data provider for Flex UI components and supports remote sorting and filtering. Complete integration with Seam’s events, both synchronous and asynchronous, allows a Flex client to observe events raised by the server. Flex validators integrated with server-side Hibernate validator, allowing validation messages either on the fly, or after remote calls. Client support for Seam conversations. A lightweight component-based Flex framework that is deeply integrated with the other features and can advantageously replace Cairngorm or PureMVC. Let's have a look at a very simple example to see how this works. Seam component (simply extracted from the Seam booking sample): @Stateful @Name("hotelSearch") @Scope(ScopeType.SESSION) @Restrict("#{identity.loggedIn}") public class HotelSearchingAction implements HotelSearching { @PersistenceContext private EntityManager em; private String searchString; private int pageSize = 10; private int page; @DataModel private List hotels; public void find() { page = 0; queryHotels(); } ... private void queryHotels() { hotels = em.createQuery( "select h from Hotel h where lower(h.name) like #{pattern} " + "or lower(h.city) like #{pattern} or lower(h.zip) like #{pattern} " + "or lower(h.address) like #{pattern}") .setMaxResults(pageSize) .setFirstResult( page * pageSize ) .getResultList(); } ... public List getHotels() { return this.hotels; } public int getPageSize() { return pageSize; } public void setPageSize(int pageSize) { this.pageSize = pageSize; } @Factory(value="pattern", scope=ScopeType.EVENT) public String getSearchPattern() { return searchString == null ? "%" : '%' + searchString.toLowerCase().replace('*', '%') + '%'; } public String getSearchString() { return searchString; } public void setSearchString(String searchString) { this.searchString = searchString; } @Remove public void destroy() {} } MXML application: [Bindable] private var tideContext:Context = Seam.getInstance().getSeamContext(); // Components initialization in a static block { Seam.getInstance().addComponents([HotelsCtl]); } [Bindable] [In] public var hotels:ArrayCollection; private function init():void { tideContext.mainAppUI = this; // Registers the application with Tide } private function search(searchString:String):void { dispatchEvent(new TideUIEvent("search", searchString)); } Tide Flex component: import mx.collections.ArrayCollection; [Name("hotelsCtl")] [Bindable] public class HotelsCtl { [In] public var hotels:ArrayCollection; [In] public var hotelSearch:Object; [Observer("searchForHotels")] public function search(searchString:String):void { hotelSearch.searchString = text; hotelSearch.find(); } } Of course, this is an overly simple example but there is close to no code which seems unnecessary while having a clean separation of concerns between the UI, the client component, and the remote service. Building Flex applications could hardly be easier. There are a lot of choices out there today for creating rich Internet applications, each with its own set of advantages. When making the decision on which path to take, you want to get started easily but without sacrificing the ability to create a robust, scalable, and maintainable application. GraniteDS maintains this balance.
Over the last few days, I had the chance to test Datameer analytics solution (das). Das is a platform for Hadoop which includes data source integration, an analytics engine, and visualization functionality. This promise of a fully integrated big data analysis process motivated me to test the product. It really includes all required functionality for data management or ETL, it provides standard tools to analyze data and there are nice ways to build visualization dashboards. For example, there are connectors for Twitter, IMAP, HDFS, or FTP available. All menus and processes are self-explaining and the complete interface is strongly Excel or spreadsheet oriented. If you are familiar with excel you can do the analyses on your big data out of the box. For a fast on-the-fly analysis performance you only work with a subset of your data and the analyses you store will then be automatically transformed into a kind of procedure. In the end – or according to a schedule you set – you “run” the analyses on your big data: Das collects the latest data for you, Das creates MapReduce jobs in the background, and updates all your spreadsheets and visualizations. To close the analysis circle you can use the connectors to write your results back to HDFS or a database such as HBase or many more technologies. Analytics the Way You Need Them There are a lot of ways that Datameer can prove useful to you, and you don’t want to discount that fact. You will discover that it is likely the case that Datameer is a great way to display a large-scale amount of data in a format that is digestible and useful to you. We can only absorb so much data as individual human beings, but we can certainly use the information that we receive to make important decisions about our business, our products, and the future experiences that our customers will have as a result. Thus, it makes sense that there are many people who want to use Datameer as a means of getting this done. If you have felt what it is like to see all of your data on a big spreadsheet and be able to visualize what it looks like coming together, then you know that you need Datameer and similar products to help you get the results that you really need. Das is really designed for big data. If you test it with small data you will be frustrated by the performance – the overhead of creating MapReduce jobs dominates in this situation. But as soon as you start with real big data analyses this overhead gets negligible and das is taking over a lot of your programming work. My Test Infrastructure The following figure provides a nice overview of the Datameer infrastructure. Das supports many data sources, it runs on all Hadoop distributions, it provides a rest API and you can add plugins as connectors for other modeling languages such as r (#rstats). I tested das version 3.1.2 running on our MapR Hadoop cluster version 3.0.2. After getting the latest package version from Datameer support the installation was straightforward and it worked out of the box. Thanks to Datameer for providing a full test license. There are several online tutorials and videos available and there are some tutorial apps. Apps are another great feature of Datameer. You can download Datameer apps which include connectors, workbooks, and visualizations for different analysis examples. And you can create your own app from your analyses and share them with your colleagues or the community. My Test Data And Analyses I tested das with the famous “airline on-time performance” data set consisting of flight arrival and departure details for all commercial flights within the USA, from October 1987 to April 2008. I downloaded all the data (including supplements) to MapR FS, created connectors for the data, and imported the data into a workbook. In the workbook I tested many classical statistical counting analyses: Grouping functionality for the airports and counting the number of flights Grouping for the airlines and calculating different statistics as mean values for the air time Using joins to add additional information like the airline name to the airline identifier Doing sorts to extract the most interesting airports depending on different measures I am not an Excel expert. so it took me some time to get used to this low-level process of doing analysis on spreadsheets. But in the end, it is a very intuitive process of creating analyses. Every new analysis will be available in a new tab in your workbook. There are several nice functionalities to support your work. For example, there is a “sheet dependencies” overview which provides information about the dependencies between sheets. Apart from the classical analyses, das provides some data mining functionality. It is called “smart analytics”. So far, it covers k-means clustering, decision trees, column dependencies, and recommendations. It works out of the box but is not yet on the level to be satisfying for real analyses. e.g. For k-means clustering, there is no support for choosing the right number of clusters (k) and you can not switch between different distance functions (default is euclidean distance). Finally, I visualized all my results in a nice “infographic”. There are many different visualization tools and parameters available. After playing around with the settings you can create a nice dashboard and share it with your colleagues. Please be aware that the complete data set is about 5 GB. Importing the data set takes about 30 minutes and running the workbook took more than 3h in my case. In the end, I split my analyses into several workbooks to improve the feasibility. Summary It was easy to get started with Datameer analytics solution (das). It is definitely a great tool to do big data analyses without any detailed Hadoop or big data knowledge. Furthermore, it covers many use cases and provides all required functionality for your daily analysis process. However, as soon as your analyses get more complex, the limitations of Datameer become apparent and you will probably look for a more powerful toolset or start implementing your big data analyses directly on Hadoop. Finally, Datameer supports many steps in the big data analysis process, it works efficiently and the usability is straightforward. But big data is more than ETL, data analysis and visualizing the results. You should never forget to think about your use case and the business value that you want to extract from your data. In the end, this is what should guide you in choosing the tools and/or implementations to use.
We take a comparative look at two of the most popular databases on the market today, CouchDB and MariaDB, and what each brings to the table for your team.
Open banking and APIs are at the heart of the digital finance revolution. Monitoring APIs helps protect banks from issues including failures and slow-downs.
Conceptual Architecture for Conversational Artificial Intelligence (AI) / Natural Language Processing (NLP) based Platform for Customer Support and Agent Services.
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