A Quick Look At Data Visualization Tools

Vincent Wong walks us through data visualization tools on the market today:

Highcharts

When we talk about Echarts, we will usually compare it with Highcharts. The relationship between them is a bit like the relationship between WPS and Office.

Highcharts is also a visualization library which you have to pay for it if you are going to use it. It has many advantages, for example, its documents and tutorials, JS scripts, and CSS are very detailed. It saves time and allows you to pay more attention to learning and developing. What’s more, it is very stable.

There are some good tools on this list.

It’s All ETL (Or ELT) In The End

Robin Moffatt notes that ETL (and ELT) doesn’t go away in a streaming world:

In the past we used ETL techniques purely within the data-warehousing and analytic space. But, if one considers why and what ETL is doing, it is actually a lot more applicable as a broader concept.

  • Extract: Data is available from a source system
  • Transform: We want to filter, cleanse or otherwise enrich this source data
  • Load: Make the data available to another application

There are two key concepts here:

  • Data is created by an application, and we want it to be available to other applications
  • We often want to process the data (for example, cleanse and apply business logic to it) before it is used

Thinking about many applications being built nowadays, particularly in the microservices and event-driven space, we recognize that what they do is take data from one or more systems, manipulate it and then pass it on to another application or system. For example, a fraud detection service will take data from merchant transactions, apply a fraud detection model and write the results to a store such as Elasticsearch for review by an expert. Can you spot the similarity to the above outline? Is this a microservice or ETL process?

Things like this are reason #1 why I expect data platform jobs (administrator and developer) to be around decades from now.  The set of tools expand, but the nature of the job remains similar.

Simplified Disaster Recovery With dbatools

Chrissy LeMaire shows how you can make DR a lot easier with dbatools:

When we talk about Disaster Recovery or DR, it’s often coupled with the term High Availability or HA. Here are some definitions from my graduate course on HADR.

high availability

  • Deals with minor outages, and failover solutions are automated
  • The goal is to restore full system functionality in a short time

disaster recovery

  • Deals with major outages such as natural and man-made disasters
  • Focuses on manual processes and procedures to restore systems back to their original state
  • Characterized by a phased approach to restoring the primary site

In the context of SQL Server, HA would be Availability Groups (AG), Failover Clustering (FCI), Log Shipping and more. I won’t be addressing High Availability in this post, however.

Chrissy has a demo of everything in action, including running a series of tests to ensure that your DR site actually has everything.

New(ish) VLF Status: 4

Paul Randal points out a new VLF status which can appear if you’re using an Availability Group:

At least since I started working on the SQL Server team (just after 7.0 shipped) and since then there have only been two VLF status codes:

  • 0 = the VLF is not active (i.e. it can be (re)activated and overwritten)
  • (1 = not used and no-one seems to remember what it used to mean)
  • 2 = the VLF is active because at least one log record in it is ‘required’ by SQL Server for some reason (e.g. hasn’t been backed up by a log backup or scanned by replication)

A few weeks ago I learned about a new VLF status code that was added back in SQL Server 2012 but hasn’t come to light until recently (at least I’ve never encountered it in the wild). I went back-and-forth with a friend from Microsoft (Sean Gallardy, a PFE and MCM down in Tampa) who was able to dig around in the code to figure out when it’s used.

Read on to uncover the mysteries of the VLF status of 4.

Dealing With Large JSON Values

Kevin Feasel

2018-09-20

T-SQL

Bert Wagner investigates an issue he found where his long JSON strings were becoming NULL in SQL Server:

After a little bit more research, I discovered that the return type for JSON_VALUE is limited to 4000 characters.   Since JSON_VALUE is in lax mode by default, if the output has more than 4000 characters, it fails silently.

To force an error in future code I could use SELECT JSON_VALUE(@json, ‘strict $.FiveThousandAs’)  so at least I would be notified immediately of an problem with my  query/data (via failure).

Although strict mode will notify me of issues sooner, it still doesn’t help me extract all of the data from my JSON property.

Read on for the answer.

Deleting Top Records With An Order By Clause

Kevin Feasel

2018-09-20

T-SQL

Kenneth Fisher shows that deleting the top N records with an ORDER BY clause is not straightforward:

Did you know you can’t do this?

DELETE TOP (10)
FROM SalesOrderDetail
ORDER BY SalesOrderID DESC;

Msg 156, Level 15, State 1, Line 8
Incorrect syntax near the keyword ‘ORDER’.

I didn’t. Until I tried it anyway. Turns out, it says so right in the limitations section of BOL. Fortunately, that same section does have a recommendation on how to move forward.

Read on for a couple of methods to do this.

Categories

September 2018
MTWTFSS
« Aug Oct »
 12
3456789
10111213141516
17181920212223
24252627282930