The general purpose of machine learning in big data is the representation of the input data and the generalization of the learned patterns for the utilization of future unseen data. The data representation has a major impact on the performance of machine learners on the data. If the data is represented in a poor manner it will reduce the performance of even an advanced, complex machine learner. The data which is represented in a better manner will lead to high performance.
The growth in big data has resulted in to develop the data pipelines to help train and deploy the ML models. They also develop more technically demanding continuous data pipelines that offer applications with artificial intelligence and ML algorithms.
Once focused on developing pipelines to support traditional data warehouses, the data engineering teams are in the process of developing more technically demanding continuous data pipelines that feed applications with artificial intelligence and machine learning algorithms. These data pipelines are very affordable, quick, and are quite dependable regardless of the kind of workload and use case.
Let’s take a look at the various frameworks often used for developing engines by machine learning using big data.
Apache Spark
It is a general purpose open-source computational engine for Hadoop data. It gives an expressive programming model, which helps a vast range of applications, such as machine learning, graph computation, and stream processing. It works really well for data sets that need a programmatic approach, like file formats that are vastly used in healthcare insurance processing.
Spark is also distributed, flexible, quick. It gives an in-memory computational engine, as well as facilities for real-time data streaming. This offers machine learning in big data to create stream processing in the same way they write batch processing. Spark also supports mid-query fault-tolerance and also actively recovers when there is a failure.
Apache Hadoop/Hive
Hive is an Apache open-source project, which is built on Hadoop and is used for analyzing, querying, and summarizing huge data sets with the help of a SQL-like interface. The Apache Hive is mainly used for batch processing and batch SQL queries. It also supports data exploration on huge volumes of unstructured, semi-structured, and structured data sets, due to its presence of inexpensive storage and its compatibility with SQL.
Hive provides diverse computational techniques; the micro-batching with Hive is considered a workable and more economical option.
Presto
It is an open-source SQL query engine, which is developed by Facebook. Presto is quite helpful for running interactive analytic queries against data sources of all sizes from gigabytes to petabytes. It was developed to offer SQL query abilities against disparate data sources that provide the users to combine data from various data sources in a single query. The machine learning using big data provides a fast and simplified way to access data from various sources with the help of industry-standard SQL query language.
Presto is also considered an ideal framework for the orchestration of data pipelines. For instance, if the information is going to be delivered as a dashboard or the intention is to probe the resulting data sets with low-latency SQL queries, in these cases, the Presto will be considered as an optimal choice. By using Presto, the queries can be run faster when compared to Spark.
Airflow
Airflow is an open-source tool, which pro grammatically author, schedule, and monitor data workflows. With the help of Airflow, the users would be able to author workflows as Directed Acyclic Graphs (DAGs). DAG is the set of tasks required to complete a pipeline organized to reflect their relationships and inter dependencies.
Airflow is naively integrated to work with growth in big data systems that include Hive, Presto, and Spark. It works best with workloads, which follow the batch processing model.
For developing a machine learning engine it is very important to choose an optimal framework depending on the certain business requirements. The machine learning using big data usually take help from several data pipelines to complete many stages of the function while building ML engines. All of the source systems need to be identified and connected. Data needs to be extracted, quality checked, and cleansed. The data preparation stage must be closely examined. Once this is defined, one can implement the actual ML algorithms to generate useful insights.