Vnext – Its SQL Server 2017

Yes thats true, SQL Server New version will be release this year and call it “SQL Server 2017” where sql can also work on Windows, MacOS and Linux

 

 

https://blogs.technet.microsoft.com/dataplatforminsider/2017/04/19/sql-server-2017-community-technology-preview-2-0-now-available/

Cooooool.

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vNext has AG

Now vNext SQL Server on Linux supports Availability Group HA/DR functionality supported.

 

https://blogs.technet.microsoft.com/dataplatforminsider/2017/02/17/sql-server-on-linux-mission-critical-hadr-with-always-on-availability-groups/

 

 

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SQL Agent on vNext – Linux

SQL Server is running on Linux now with SQLPAL- SQL Platform Abstraction Layer(SQLPAL) -it will work as a virtual Windows server on Linux so I think now Microsoft should able to include things which we are doing on Windows Server.

on that note, Now vNext with CTP 1.4 Microsoft has introduce the SQL Server Agent functionality on vNext.

 

https://blogs.technet.microsoft.com/dataplatforminsider/2017/03/17/sql-server-on-linux-running-jobs-with-sql-server-agent/

 

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SQL Server on Linux

Yes that is true, now SQL Server is on Linux.

https://www.microsoft.com/en-in/sql-server/sql-server-vnext-including-Linux

Download the public preview

https://docs.microsoft.com/en-us/sql/linux/

I am learning it and would love to write more blogs soon.

 

 

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Future DBA – Big Data -MongoDB 2

In our earlier blog we discussed an introduction to MongoDB. so, how is MongoDB is into BigData, MongoDB has a concept called sharding with replication, so here using sharding it uses a cluster like configuration and data will be load-balance- equally distribute to multiple shard with the shard key.

so if we consider the HDFS- hadoop concept here unlike named node we have config server and shard is like data node. but here we have data is distributed to multiple shards but in hadoop system data is replicated to multiple data nodes. and MongoDB maintain the redundancy by using Replication.

Balancer makes sure that data is distruted equally to all the shards if data is not balanced balancer will run the processes at background and balance the data.

here shard key plays a very important role.

will write more on it later.

 

 

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Future DBA – Big Data -MongoDB 1

We have discussed hadoop and its HDFS management tools for big data system, which works horizontal scaling for data distribution and can be used for data warehousing. can manage big data/heavy data

There is another BIG Data system called MongoDB, which is again an open source. this is very developer friendly NoSQL system. as you know about RDBMS it has pre defined record structure and rows size is static. so for developer if they want to think and make some changes in the metadata by adding any columns or make changes in data type of the column will intern has to make the changes into the complete data and its related indexes. MongoDB is document oriented NoSQL.

MongoDB consider record as a document and you can dynamically add the columns into it and for developer its not necessary to input all the column information into one record/document. this way developer likes this database system.

MongoDB is written in C, C++, and java scripts. and it works same as developments.

https://en.wikipedia.org/wiki/MongoDB

 

 

 

 

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Future DBA – Hive Big Data 2

In our last blog on Future DBA, we discussed on HADOOP -HDFS system. how as we know HDFS management is quite difficult so with the help of Vendors -Cloudera/Hortonworks/MapR we can integrate the tools/utility in a GUI way and can be manage easily and efficiently.

This HDFS data can be retrieved and inserted using HIVE utility which will provide us the access to HDFS data in a SQL like way and we can create a access the data just like sql queries.

Hive requires the Meta store system, can be any RDBMS opensource -MySQL or PostgreSQL or any other RDBMS which will store the metadata on the HIVE and actual data would be stored in HDFS.

HIVE uses Map-Reduce process for retrieving data from HDFS.

So for DBA we can work on HDFS data efficiently and just like our RDBMS.

 

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Future DBA – Hadoop Big Data 1

In our last blog on Hadoop Big Data we have discussed about Hadoop and its tools/utility to connect to HDFS. on continue to that hadoop is mostly for big data and the on hadoop is stored in HDFS with named node contains pointers /address of data location and data stores contains the actual data. there are multiple data stores and the data on data store is replicated to multiple data nodes for redundancy purpose. and accessing the data on multiple nodes would be faster.

we can store or use one node static to store data as a backup node. Hadoop is used for data warehouse purpose and as its a BIG Data, data stored on it is in bulk /huge and used for read purpose. so if we use hive/impala or any other tool HDFS data can be mostly be used for READ-ONLY data warehouse and used to generate the report and get the data once data is inserted into HDFS. There are mappers to read the data on data nodes.

*HDFS /BIG DATA is effective on data reads and not work best for UPDATES.

 

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Future DBA – Hadoop Big Data

As we discussed on earlier blog that in future its more than SQL-(R)DBMS, as data is growing we have to know BIGDATA as well, and when we talk about BIGDATA we should be aware of HADOOP.

I have gone through many blogs and webcasts but its little complicated on hadoop related stuff. but I want to go for concept and easy way of learning. this blog is not an deep dive but an overview of Hadoop. Hadoop works on HDFS(Hadoop Distributed File System), where it has Named node and Data node. Named node contains the metadata information where which data stores, and Data node contains actual data.Name node should be high capacity cluster with big configuration and data nodes can be a multiple (Many..Many) stand alone system to distribute the data on multiple servers… that’s call “Distributed File System”.

Working on HDFS is quite difficult requires MapReduce programming and retrieving and saving data on it is requires expertise in programming, so to overcome it there are several supported tools been used, Some of them are as follows:

  • Apache Pig
    Apache Hive
    Apache HBase
    Apache Phoenix
    Apache Spark
    Apache ZooKeeper
    Cloudera Impala
    Apache Flume
    Apache Sqoop
    Apache Oozie
    Apache Storm

All the above products are open source (Apache) and do not have vendor support.

 

There are 3 Vendors who has worked on this open source and build the enterprise product and they provide support to HDFS system they are as follows:

Cloudera – using the Cloudera Director Plugin for Google Cloud Platform

Hortonworks – using bdutil support for Hortonworks HDP

MapR – using bdutil support for MapR

This is the basic and quite important information if you want to go with Hadoop system. so when we see these tools we should know that these are based on Hadoop file system-HDFS.

I will talk more on these in future blogs also will write on other NoSQL technologies, Like MongoDB which doesnot use HDFS.

I just started writing on BIG Data/NoSQL. so Appriciate your comment/feedback

Reference:

https://en.wikipedia.org/wiki/Apache_Hadoop#cite_note-8

 

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Operational Analytics -SQL Server 2016

What is Operational Analytics:-

Operational Analytics is a combination of two words “Operational” and “Analytics”. so your OLTP system is a operational system where day to day task eg order table keeps on updating. and Analytics is a OLAP where analysis of the order table can be done after ETL – moving the operational data with the help of nightly jobs… or other ways to OLAP system eg. Analysis services or BI system and analyse OLTP data, which is used by manager or decision maker and decide.

So earlier days when we have to analyse the data we have to wait for some time as querying on the OLTP system is quite expensive and cost a lot and makes system hung due to Locking and un-compatible locks.

Now Management team wanted to analyse the data as soon as any order takes place to decide how things are happening and understand the system and decide on it.

hence “Operationsl Analytics” is place and both system or task can be done at a time. this has been incorporated by other system, so as SQL Server.

SQL Server can achieve this in SQL Server 2016 with the help of :

  • In-Memory System.
  • Updateable Non cluster Column store Index (NCCI)
  • Compression Delay (Filtered Indexes)

So considering the critical/hot data in in-memory tables and use those tables as a NCCI and use compression delay so that the column store data will be compressed after that delay to maintain if that data is getting changes.

the detail is in following blog:

https://msdn.microsoft.com/en-IN/library/dn817827.aspx

this is happening things and would like to write more on it.

*btw: Sunil Agarwal has written/webcast quite more on this.

https://social.technet.microsoft.com/Profile/Sunil%2bAgarwal/activity

https://blogs.technet.microsoft.com/dataplatforminsider/

 

 

 

 

 

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