Java Odbc

The REST service can help developers to provide services to end users with a simple and unified interface. However, in the application scenario of data analysis, some mature data analysis tools (such as Tableau, Excel, etc.) require the user to provide an ODBC data source, in which case the REST service does not meet the user's need for data usage. This article provides a detailed overview of how to develop a custom ODBC driver based on the existing rest service from an implementation perspective. The article focuses on the introduction of ODBC ...

Open standards can be used via Optim http://www.aliyun.com/zixun/aggregation/13722.html ">open Data Manager,optim" (such as ODBC, JDBC, or XML) provides continuous access to archived data. Learn the difference between the available methods for accessing Optim archived data in the linux®/unix® environment, and how to do so in a Linux environment ...

In large data technology, Apache Hadoop and MapReduce are the most user-focused. But it's not easy to manage a Hadoop Distributed file system, or to write MapReduce tasks in Java. 　　 　　Then Apache hive may help you solve the problem. The Hive Data Warehouse tool is also a project of the Apache Foundation, one of the key components of the Hadoop ecosystem, which provides contextual query statements, i.e. hive queries ...

As we all know, the big data wave is gradually sweeping all corners of the globe. And Hadoop is the source of the Storm's power. There's been a lot of talk about Hadoop, and the interest in using Hadoop to handle large datasets seems to be growing. Today, Microsoft has put Hadoop at the heart of its big data strategy. The reason for Microsoft's move is to fancy the potential of Hadoop, which has become the standard for distributed data processing in large data areas. By integrating Hadoop technology, Microso ...

The operating language of the data is SQL, so many tools are developed with the goal of being able to use SQL on Hadoop. Some of these tools are simply packaged on top of the MapReduce, while others implement a complete data warehouse on top of the HDFs, while others are somewhere between the two. There are a lot of such tools, Matthew Rathbone, a software development engineer from Shoutlet, recently published an article outlining some common tools and scenarios for each tool and not ...

Basically are in group discussion, when others ask the introductory questions, later thought of new problems to add in. 　　But the problem of getting started is also very important, the understanding of the principle determines the degree of learning can be in-depth. 　　Hadoop is not discussed in this article, only peripheral software is introduced. 　　Hive: This is the most software I've ever asked, and it's also the highest utilization rate around Hadoop. 　　What the hell is hive? How to strictly define hive is really not too easy, usually for non-Hadoop professionals ...

Now Apache Hadoop has become the driving force behind the development of the big data industry. Techniques such as hive and pig are often mentioned, but they all have functions and why they need strange names (such as Oozie,zookeeper, Flume). Hadoop has brought in cheap processing of large data (large data volumes are usually 10-100GB or more, with a variety of data types, including structured, unstructured, etc.) capabilities. But what's the difference? Today's enterprise Data Warehouse ...

Now Apache Hadoop has become the driving force behind the development of the big data industry. Techniques such as hive and pig are often mentioned, but they all have functions and why they need strange names (such as Oozie,zookeeper, Flume). Hadoop has brought in cheap processing of large data (large data volumes are usually 10-100GB or more, with a variety of data types, including structured, unstructured, etc.) capabilities. But what's the difference? Today's enterprise data warehouses and relational databases are good at dealing with ...

Now Apache Hadoop has become the driving force behind the development of the big data industry. 　　Techniques such as hive and pig are often mentioned, but they all have functions and why they need strange names (such as Oozie,zookeeper, Flume). Hadoop has brought in cheap processing of large data (large data volumes are usually 10-100GB or more, with a variety of data types, including structured, unstructured, etc.) capabilities. 　　But what's the difference? Enterprise Data Warehouse and relational number today ...

Now Apache Hadoop has become the driving force behind the development of the big data industry. 　　Techniques such as hive and pig are often mentioned, but they all have functions and why they need strange names (such as Oozie,zookeeper, Flume). Hadoop has brought in cheap processing of large data (large data volumes are usually 10-100GB or more, with a variety of data types, including structured, unstructured, etc.) capabilities. 　　But what's the difference? Enterprise Data Warehouse and relational number today ...