Chhavi Joshi is right on the money. A database is both a query execution tool and a data storage backend. HAWQ is executing against native Hadoop storage, i.e. HBase, HDFS, etc.
Robert L Marshall Senior Consultant | Avalon Consulting, LLC <http://www.avalonconsult.com/>c: (210) 853-7041 LinkedIn <http://www.linkedin.com/company/avalon-consulting-llc> | Google+ <http://www.google.com/+AvalonConsultingLLC> | Twitter <https://twitter.com/avalonconsult> ------------------------------------------------------------------------------------------------------------- This message (including any attachments) contains confidential information intended for a specific individual and purpose, and is protected by law. If you are not the intended recipient, you should delete this message. Any disclosure, copying, or distribution of this message, or the taking of any action based on it, is strictly prohibited. On Fri, Nov 13, 2015 at 10:41 AM, Chhavi Joshi < [email protected]> wrote: > If you have HAWQ greenplum integration you can create the external tables > in greenplum like HIVE. > > For uploading the data into tables just need to put the file into > hdfs.(same like external tables in HIVE) > > > > > > I still believe HAWQ is only the SQL query engine not a database. > > > > Chhavi > > *From:* Atri Sharma [mailto:[email protected]] > *Sent:* Friday, November 13, 2015 3:53 AM > > *To:* [email protected] > *Subject:* Re: what is Hawq? > > > > Greenplum is open sourced. > > The main difference is between the two engines is that HAWQ is more for > Hadoop based systems whereas Greenplum is more towards regular FS. This is > a very high level difference between the two, the differences are more > detailed. But a single line difference between the two is the one I wrote. > > On 13 Nov 2015 14:20, "Adaryl "Bob" Wakefield, MBA" < > [email protected]> wrote: > > Is Greenplum free? I heard they open sourced it but I haven’t found > anything but a community edition. > > > > Adaryl "Bob" Wakefield, MBA > Principal > Mass Street Analytics, LLC > 913.938.6685 > www.linkedin.com/in/bobwakefieldmba > Twitter: @BobLovesData > > > > *From:* dortmont <[email protected]> > > *Sent:* Friday, November 13, 2015 2:42 AM > > *To:* [email protected] > > *Subject:* Re: what is Hawq? > > > > I see the advantage of HAWQ compared to other Hadoop SQL engines. It looks > like the most mature solution on Hadoop thanks to the postgresql based > engine. > > > > But why wouldn't I use Greenplum instead of HAWQ? It has even better > performance and it supports updates. > > > Cheers > > > > 2015-11-13 7:45 GMT+01:00 Atri Sharma <[email protected]>: > > +1 for transactions. > > I think a major plus point is that HAWQ supports transactions, and this > enables a lot of critical workloads to be done on HAWQ. > > On 13 Nov 2015 12:13, "Lei Chang" <[email protected]> wrote: > > > > Like what Bob said, HAWQ is a complete database and Drill is just a query > engine. > > > > And HAWQ has also a lot of other benefits over Drill, for example: > > > > 1. SQL completeness: HAWQ is the best for the sql-on-hadoop engines, can > run all TPCDS queries without any changes. And support almost all third > party tools, such as Tableau et al. > > 2. Performance: proved the best in the hadoop world > > 3. Scalability: high scalable via high speed UDP based interconnect. > > 4. Transactions: as I know, drill does not support transactions. it is a > nightmare for end users to keep consistency. > > 5. Advanced resource management: HAWQ has the most advanced resource > management. It natively supports YARN and easy to use hierarchical resource > queues. Resources can be managed and enforced on query and operator level. > > > > Cheers > > Lei > > > > > > On Fri, Nov 13, 2015 at 9:34 AM, Adaryl "Bob" Wakefield, MBA < > [email protected]> wrote: > > There are a lot of tools that do a lot of things. Believe me it’s a full > time job keeping track of what is going on in the apache world. As I > understand it, Drill is just a query engine while Hawq is an actual > database...some what anyway. > > > > Adaryl "Bob" Wakefield, MBA > Principal > Mass Street Analytics, LLC > 913.938.6685 > www.linkedin.com/in/bobwakefieldmba > Twitter: @BobLovesData > > > > *From:* Will Wagner <[email protected]> > > *Sent:* Thursday, November 12, 2015 7:42 AM > > *To:* [email protected] > > *Subject:* Re: what is Hawq? > > > > Hi Lie, > > Great answer. > > I have a follow up question. > Everything HAWQ is capable of doing is already covered by Apache Drill. > Why do we need another tool? > > Thank you, > Will W > > On Nov 12, 2015 12:25 AM, "Lei Chang" <[email protected]> wrote: > > > > Hi Bob, > > > > Apache HAWQ is a Hadoop native SQL query engine that combines the key > technological advantages of MPP database with the scalability and > convenience of Hadoop. HAWQ reads data from and writes data to HDFS > natively. HAWQ delivers industry-leading performance and linear > scalability. It provides users the tools to confidently and successfully > interact with petabyte range data sets. HAWQ provides users with a > complete, standards compliant SQL interface. More specifically, HAWQ has > the following features: > > · On-premise or cloud deployment > > · Robust ANSI SQL compliance: SQL-92, SQL-99, SQL-2003, OLAP > extension > > · Extremely high performance. many times faster than other Hadoop > SQL engine. > > · World-class parallel optimizer > > · Full transaction capability and consistency guarantee: ACID > > · Dynamic data flow engine through high speed UDP based > interconnect > > · Elastic execution engine based on virtual segment & data > locality > > · Support multiple level partitioning and List/Range based > partitioned tables. > > · Multiple compression method support: snappy, gzip, quicklz, RLE > > · Multi-language user defined function support: python, perl, > java, c/c++, R > > · Advanced machine learning and data mining functionalities > through MADLib > > · Dynamic node expansion: in seconds > > · Most advanced three level resource management: Integrate with > YARN and hierarchical resource queues. > > · Easy access of all HDFS data and external system data (for > example, HBase) > > · Hadoop Native: from storage (HDFS), resource management (YARN) > to deployment (Ambari). > > · Authentication & Granular authorization: Kerberos, SSL and role > based access > > · Advanced C/C++ access library to HDFS and YARN: libhdfs3 & > libYARN > > · Support most third party tools: Tableau, SAS et al. > > · Standard connectivity: JDBC/ODBC > > > > And the link here can give you more information around hawq: > https://cwiki.apache.org/confluence/display/HAWQ/About+HAWQ > > > > > > And please also see the answers inline to your specific questions: > > > > On Thu, Nov 12, 2015 at 4:09 PM, Adaryl "Bob" Wakefield, MBA < > [email protected]> wrote: > > Silly question right? Thing is I’ve read a bit and watched some YouTube > videos and I’m still not quite sure what I can and can’t do with Hawq. Is > it a true database or is it like Hive where I need to use HCatalog? > > > > It is a true database, you can think it is like a parallel postgres but > with much more functionalities and it works natively in hadoop world. > HCatalog is not necessary. But you can read data registered in HCatalog > with the new feature "hcatalog integration". > > > > Can I write data intensive applications against it using ODBC? Does it > enforce referential integrity? Does it have stored procedures? > > > > ODBC: yes, both JDBC/ODBC are supported > > referential integrity: currently not supported. > > Stored procedures: yes. > > > > B. > > > > > > Please let us know if you have any other questions. > > > > Cheers > > Lei > > > > > > > > > > ------------------------------ > > ============================================================================================================================ > Disclaimer: This message and the information contained herein is > proprietary and confidential and subject to the Tech Mahindra policy > statement, you may review the policy at > http://www.techmahindra.com/Disclaimer.html externally > http://tim.techmahindra.com/tim/disclaimer.html internally within > TechMahindra. > > ============================================================================================================================ > >
