Google announces improvements in BigQuery, the Big Data management tool launched by the Internet giant in 2010 as a service capable of analyzing big data inside and outside the ecosystem of Apache Hadoop. Despite this, with these deep updates, the Cloud ServiceThe "Cloud Service" refers to the delivery of computing resources over the Internet, allowing users to access storage, processing and applications without the need for on-premises physical infrastructure. This model offers flexibility, Scalability and cost savings, since companies only pay for what they use. What's more, Facilitates collaboration and data access from anywhere, improving operational efficiency in various industries.. strengthens its independent operation and seeks to attract Hadoop users, the current leader in Big Data analytics.
While the second generation of Hadoop tries to overcome its weaknesses such as lack of speed and complexity, while reinforcing its obvious advantages, Google focuses the development of BigQuery towards the commercialization of the service as alternative to that.
Even though BigQuery is compatible with Hadoop and both products have been created directly or indirectly by Google, their paths don't seem destined to keep crossing. If they did so far, despite this Google shows that it wants to separate them more and more to boost its competitive advantage on all fronts, including its rivalry with AWS Kinesis.
In reality, BigQuery aims to be a viable alternative to the open-source option presented by MapReduceMapReduce is a programming model designed to efficiently process and generate large data sets. Powered by Google, This approach breaks down work into smaller tasks, which are distributed among multiple nodes in a cluster. Each node processes its part and then the results are combined. This method allows you to scale applications and handle massive volumes of information, being fundamental in the world of Big Data.... Y Hadoop Distributed File SystemThe Hadoop Distributed File System (HDFS) is a critical part of the Hadoop ecosystem, Designed to store large volumes of data in a distributed manner. HDFS enables scalable storage and efficient data management, splitting files into blocks that are replicated across different nodes. This ensures availability and resilience to failures, facilitating the processing of big data in big data environments.... (HDFSHDFS, o Hadoop Distributed File System, It is a key infrastructure for storing large volumes of data. Designed to run on common hardware, HDFS enables data distribution across multiple nodes, ensuring high availability and fault tolerance. Its architecture is based on a master-slave model, where a master node manages the system and slave nodes store the data, facilitating the efficient processing of information..). With the deep update that, among other improvements, makes it possible to combine query results from multiple data tables, Google intends to exploit the speed and the real-time analysis provided by Dremel, the product on which the BigQuery design is based.
Cloud data analysis
Conceived as a service that facilitates quick query in the cloud after the user submits data to Google through the BigQuery API, your update continues to focus on queries of type SQL. In this new version, new capabilities are added along with the previously mentioned function of joining data from multiple tables in a single query through a new clause JOIN"JOIN" is a fundamental operation in databases that allows you to combine records from two or more tables based on a logical relationship between them. There are different types of JOIN, as INNER JOIN, LEFT JOIN and RIGHT JOIN, each with its own characteristics and uses. This technique is essential for complex queries and more relevant and detailed information from multiple data sources...., no limit on data size.
Until now, BigQuery could only handle data groups of a maximum of 8 MB and, Besides, add functionalities to import timestamps from other systems, query datetime data or add columns to existing tables and receive automatic emails when they are given access to more data sets.
In the words of Ju-kay Kwek, Product Manager, changes translate into more speed, simplicity and ease of use:
Nowadays, with BigQuery, business ideas can be obtained directly through SQL-like queries, with less effort and at a much higher speed than was previously feasible. Joining tables of terabyte data has traditionally been a difficult task for analysts, since up to now it required sophisticated development skills from MapReduce, powerful hardware and a lot of time.
Its use is totally unrelated to the yellow elephant frame, considering dispensing with it as one more advantage of the product. From Google They comment that instead of installing Hadoop, using BigQuery will save money by paying just for each query rather than the IT cost of the infrastructure required to implement it. With that and with everything, equally, Hadoop was created in its day from technologies such as MapReduce and Google File to process large amounts of data at very low cost.
Microsoft SQL and Hadoop technology
For his part, Microsoft has recently presented its Big Data solutions from the cloud in favor of the Internet of things. Starting from a single platform for data management and analysis, his use of Hadoop is part of one of his major innovations: un SQL Server 2014 faster and its Intelligent Systems Service (IIS) y Analytics Platform System (APS).
The latest version of APS is a low-cost product thanks to the combination of the technology of Hadoop y Microsoft SQL to offer a Data Warehouse that stores and manages traditional data along with the latest generation.
As a new Azure service, se presentó Microsoft Azure Intelligent System Service (ISS), a tool designed to operate from any operating system in order to take advantage of the information generated very early. different sources, like machines, sensors or devices. in addition, CCC is made available thanks to tools like Power BI for Office 365 that make it possible to combine local data and cloud data in a complementary way, resulting in rapid information management.
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