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A big data architecture is designed to handle the ingestion, processing, and analysis of data that is too large or complex for traditional database systems. The threshold at which organizations enter into the big data realm differs, depending on the capabilities of the users and their tools. For some, it can mean hundreds of gigabytes of data, while for others it means hundreds of terabytes. As tools for working with big datasets advance, so does the meaning of big data. More and more, this term relates to the value you can extract from your data sets through advanced analytics, rather than strictly the size of the data, although in these cases they tend to be quite large.
Over the years, the data landscape has changed. What you can do, or are expected to do, with data has changed. The cost of storage has fallen dramatically, while the means by which data is collected keeps growing. Some data arrives at a rapid pace, constantly demanding to be collected and observed. Other data arrives more slowly, but in very large chunks, often in the form of decades of historical data. You might be facing an advanced analytics problem, or one that requires machine learning. These are challenges that big data architectures seek to solve.
Data storage. Data for batch processing operations is typically stored in a distributed file store that can hold high volumes of large files in various formats. This kind of store is often called a data lake. Options for implementing this storage include Azure Data Lake Store or blob containers in Azure Storage.
Batch processing. Because the data sets are so large, often a big data solution must process data files using long-running batch jobs to filter, aggregate, and otherwise prepare the data for analysis. Usually these jobs involve reading source files, processing them, and writing the output to new files. Options include running U-SQL jobs in Azure Data Lake Analytics, using Hive, Pig, or custom Map/Reduce jobs in an HDInsight Hadoop cluster, or using Java, Scala, or Python programs in an HDInsight Spark cluster.
Real-time message ingestion. If the solution includes real-time sources, the architecture must include a way to capture and store real-time messages for stream processing. This might be a simple data store, where incoming messages are dropped into a folder for processing. However, many solutions need a message ingestion store to act as a buffer for messages, and to support scale-out processing, reliable delivery, and other message queuing semantics. This portion of a streaming architecture is often referred to as stream buffering. Options include Azure Event Hubs, Azure IoT Hub, and Kafka.
Stream processing. After capturing real-time messages, the solution must process them by filtering, aggregating, and otherwise preparing the data for analysis. The processed stream data is then written to an output sink. Azure Stream Analytics provides a managed stream processing service based on perpetually running SQL queries that operate on unbounded streams. You can also use open source Apache streaming technologies like Spark Streaming in an HDInsight cluster.
Machine learning. Reading the prepared data for analysis (from batch or stream processing), machine learning algorithms can be used to build models that can predict outcomes or classify data. These models can be trained on large datasets, and the resulting models can be used to analyze new data and make predictions. This can be done using Azure Machine Learning
Analytical data store. Many big data solutions prepare data for analysis and then serve the processed data in a structured format that can be queried using analytical tools. The analytical data store used to serve these queries can be a Kimball-style relational data warehouse, as seen in most traditional business intelligence (BI) solutions. Alternatively, the data could be presented through a low-latency NoSQL technology such as HBase, or an interactive Hive database that provides a metadata abstraction over data files in the distributed data store. Azure Synapse Analytics provides a managed service for large-scale, cloud-based data warehousing. HDInsight supports Interactive Hive, HBase, and Spark SQL, which can also be used to serve data for analysis.
Analysis and reporting. The goal of most big data solutions is to provide insights into the data through analysis and reporting. To empower users to analyze the data, the architecture may include a data modeling layer, such as a multidimensional OLAP cube or tabular data model in Azure Analysis Services. It might also support self-service BI, using the modeling and visualization technologies in Microsoft Power BI or Microsoft Excel. Analysis and reporting can also take the form of interactive data exploration by data scientists or data analysts. For these scenarios, many Azure services support analytical notebooks, such as Jupyter, enabling these users to leverage their existing skills with Python or R. For large-scale data exploration, you can use Microsoft R Server, either standalone or with Spark.
Orchestration. Most big data solutions consist of repeated data processing operations, encapsulated in workflows, that transform source data, move data between multiple sources and sinks, load the processed data into an analytical data store, or push the results straight to a report or dashboard. To automate these workflows, you can use an orchestration technology such Azure Data Factory or Apache Oozie and Sqoop.
When working with very large data sets, it can take a long time to run the sort of queries that clients need. These queries can't be performed in real time, and often require algorithms such as MapReduce that operate in parallel across the entire data set. The results are then stored separately from the raw data and used for querying.
A batch layer (cold path) stores all of the incoming data in its raw form and performs batch processing on the data. The result of this processing is stored as a batch view.
The batch layer feeds into a serving layer that indexes the batch view for efficient querying. The speed layer updates the serving layer with incremental updates based on the most recent data.
Data that flows into the hot path is constrained by latency requirements imposed by the speed layer, so that it can be processed as quickly as possible. Often, this requires a tradeoff of some level of accuracy in favor of data that is ready as quickly as possible. For example, consider an IoT scenario where a large number of temperature sensors are sending telemetry data. The speed layer may be used to process a sliding time window of the incoming data.
Data flowing into the cold path, on the other hand, is not subject to the same low latency requirements. This allows for high accuracy computation across large data sets, which can be very time intensive.
Eventually, the hot and cold paths converge at the analytics client application. If the client needs to display timely, yet potentially less accurate data in real time, it will acquire its result from the hot path. Otherwise, it will select results from the cold path to display less timely but more accurate data. In other words, the hot path has data for a relatively small window of time, after which the results can be updated with more accurate data from the cold path.
The raw data stored at the batch layer is immutable. Incoming data is always appended to the existing data, and the previous data is never overwritten. Any changes to the value of a particular datum are stored as a new timestamped event record. This allows for recomputation at any point in time across the history of the data collected. The ability to recompute the batch view from the original raw data is important, because it allows for new views to be created as the system evolves.
The kappa architecture was proposed by Jay Kreps as an alternative to the lambda architecture. It has the same basic goals as the lambda architecture, but with an important distinction: All data flows through a single path, using a stream processing system.
There are some similarities to the lambda architecture's batch layer, in that the event data is immutable and all of it is collected, instead of a subset. The data is ingested as a stream of events into a distributed and fault tolerant unified log. These events are ordered, and the current state of an event is changed only by a new event being appended. Similar to a lambda architecture's speed layer, all event processing is performed on the input stream and persisted as a real-time view.
If you need to recompute the entire data set (equivalent to what the batch layer does in lambda), you simply replay the stream, typically using parallelism to complete the computation in a timely fashion.
From a practical viewpoint, Internet of Things (IoT) represents any device that is connected to the Internet. This includes your PC, mobile phone, smart watch, smart thermostat, smart refrigerator, connected automobile, heart monitoring implants, and anything else that connects to the Internet and sends or receives data. The number of connected devices grows every day, as does the amount of data collected from them. Often this data is being collected in highly constrained, sometimes high-latency environments. In other cases, data is sent from low-latency environments by thousands or millions of devices, requiring the ability to rapidly ingest the data and process accordingly. Therefore, proper planning is required to handle these constraints and unique requirements.
Event-driven architectures are central to IoT solutions. The following diagram shows a possible logical architecture for IoT. The diagram emphasizes the event-streaming components of the architecture.
Devices might send events directly to the cloud gateway, or through a field gateway. A field gateway is a specialized device or software, usually collocated with the devices, that receives events and forwards them to the cloud gateway. The field gateway might also preprocess the raw device events, performing functions such as filtering, aggregation, or protocol transformation.
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