Fast Turnaround From Ingest to Insight
When you’re trying to glean wisdom from large troves of data, fast end-to-end turnaround time is a challenge because most data requires significant clean-up or format-munging before you can query it in a useful way. For teams that use an ApacheTM HadoopTM cluster to perform data cleanup and transformation, significant time and effort is often spent moving large amounts of data between the Hadoop cluster and the database. To address this bottleneck, Teradata Aster provides Aster-Hadoop Adaptor, the fastest way to move data from Hadoop into Aster Database.
For example, a web analytics team can use Hadoop to perform the initial data pre-processing that loads and cleans the data from its many web server logs and application logs. Once the data is cleaned, the Aster-Hadoop Adaptor can seamlessly copy the resulting data into Aster Database, where analysts can easily join it with other data and write and run powerful path-analysis queries in Aster Database’s familiar SQL environment with SQL-MapReduce® analytical functions.
Key Advantages of Aster-Hadoop Adaptor
Aster-Hadoop Adaptor consists of SQL-MapReduce functions that provide ultra-fast, two-way data loading between Hadoop Distributed File System (HDFS) and Aster Database. The adaptor has the following features:
- Scalable: Built on Aster’s patented SQL-MapReduce framework, so the adaptor scales with the size of your cluster and takes advantage of the distributed nature of your database tables in Aster.
- Flexible: Deploying Aster Database and Hadoop together gives you the maximum flexibility in choosing your data clean-up tools: Use any combination of functions you have built in Hadoop, functions you have built in Aster Database, and off-the-shelf functions from Aster’s extensive SQL-MapReduce library.
- Cost-effective: Enables you to use Hadoop for your initial refining and data cleanup tasks, so you can cut your overall cost of handling big data.
- Easy to use: An analyst invokes a simple SQL command in Aster Database to import the results of a Hadoop-MapReduce job, allowing deeper analysis of that data in Aster Database. The adaptor automatically parallelizes the load operation, so no specialized Hadoop knowledge is required.
- Familiar SQL syntax: Analysts use familiar SQL SELECT statements and distributed table structures to ensure that imported data is distributed evenly in the cluster, or to ensure that exported data is streamed in an even, parallel fashion to Hadoop.
- Data Consistency: Aster Database’s data integrity and transactional consistency capabilities treat the data load operation as an SQL transaction, ensuring that the data load or export is always consistent and can be carried out while other queries are running in parallel in Aster Database.
- Extensibility: Your analytics team can easily augment the adaptor functions using SQL-MapReduce to provide further customization that suits your environment and workload.
To learn more about the Aster-Hadoop Adaptor, contact us by phone at 1.888.Aster.Data, or e-mail firstname.lastname@example.org.