of data creates a direct relationship between information and time. This has very interesting advantages such as the ability to create different views of the same set of data, the ability to delete or deactivate such views as they become obsolete, greater certainty regarding the data consistency and the ability to retrieve information that is damaged by a programming error.User Address TimestampJoseLins-SPMarco/1983MariaSanto Angelo-RSSetembro/1986MariaFlorianopolis-SCFevereiro/2015JoseFlorianopolis-SCFevereiro/2015Table 1. Data ImmutabilityAlthough it is not revolutionary in itself, the Lambda architecture offers a good way to organize thinking software architect and facilitate the exchange of information on development projects. Still, some questions may arise when we see this model. We can list the most important: In the real world, what kind of application you can use the Lambda architecture? MapReduce and Hadoop are not enough for Big Data? If the Hadoop is not enough, that tool will use? If possible, why not make all processing in real time?The market is moving toward the Fast Data, and many analysts (http://blogs.wsj.com/cio/2015/01/06/fast-data-applications-emerge-to-manage-real-time-data/) are beginning to discuss what are the requirements of this new stage of Big Data. Among the applications that can benefit from this innovation include: Context dependent applications Applications dependent on the user's location Emergency Applications Social networksIn the latter case, many social networks now offer a real-time experience, we can see this when we use twitter or share something on Facebook. On the other hand, there are applications such as Waze and Google itself not offer a real-time update. These applications - probably for some design decision - have a closer MapReduce behavior, since they are capable of attending a tsunami information with high throughput, but with a high latency - that is, new information takes some time to be available.Therefore, it is important to note, that even being revolutionary, is a huge mistake to use MapReduce paradigm as a solution of all computational problems. This is because, by design this paradigm was developed as a solution to a very specific problem: increasing throughput in data analysis. In other words, the Map Reduce - and frameworks that implement it, such as Hadoop - were thought to analyze a huge amount of data. However, this increase in throughput does not necessarily imply an increase in the speed of this analysis.Many tools are being developed to solve this problem. Among these tools, we can highlight: Apache Storm (https://storm.apache.org/), also developed by Nathan Marz, offers an interesting abstraction for developing real-time applications. His idea is to create a cluster in which developers can publish topologies responsible for performing tasks. As illustrated in Figure 4, each topology consists of two components: the spouts, which are responsible for receiving the streaming data; and bolts that will process this data. Furthermore, the basic information element which flows in this architecture is called a tuple.Figure 4. Apache Storm (found in https://storm.apache.org/) Apache Kafka (http://kafka.apache.org/), developed in Scala at LinkedIn, is a highly scalable messaging system and in real time. The idea is to create a broker that is located between producers and consumers of messages to manage and accelerate the data analysis. The company Confluent