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18 February 2017
⛏️Data Management: Data Migration (Definitions)
15 February 2017
⛏️Data Management: Data Architecture (Definitions)
14 February 2017
⛏️Data Management: Data Asset (Definitions)
[information asset:] "Data in any form or media placed into meaningful context for users, collected in relation to business or research activity." (DAMA International, "The DAMA Dictionary of Data Management", 2011)
"The term data asset refers generically to organizational data, or to any transactional system, data storage system, or application that contains data required for business functions. Any of these systems might be involved in the production and use of data and therefore may affect its quality. Treating data as an asset enables organizations to manage and increase its value. The quality of the data in a system has direct effects on the successful execution of business functions and therefore the success of the enterprise as a whole." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)
[information asset:] "digitally stored content owned by an individual or organization." ( Manish Agrawal, "Information Security and IT Risk Management", 2014)
"Any entity that is comprised of data. For example, a database is a data asset that is comprised of data records. A data asset may be a system or application output file, database, document, or web page. A data asset also includes a service that may be provided to access data from an application. For example, a service that returns individual records from a database would be a data asset. Similarly, a web site that returns data in response to specific queries would be a data asset." (CNSSI 4009-2015)
"Data assets
refer to a system, application output file, document, database, or web page
that companies use to generate revenues." (CFI) [source]
12 February 2017
⛏️Data Management: Data Quality (Definitions)
11 February 2017
⛏️Data Management: Data Collection (Definitions)
"The gathering of information through focus groups, interviews, surveys, and research as required to develop a strategic plan." (Teri Lund & Susan Barksdale, "10 Steps to Successful Strategic Planning", 2006)
"The process of gathering raw or primary specific data from a single source or from multiple sources." (Adrian Stoica et al, "Field Evaluation of Collaborative Mobile Applications", 2008)
"A combination of human activities and computer processes that get data from sources into files. It gets the file data using empirical methods such as questionnaire, interview, observation, or experiment." (Jens Mende, "Data Flow Diagram Use to Plan Empirical Research Projects", 2009)
"A systematic process of gathering and measuring information about the phenomena of interest." (Kaisa Malinen et al, "Mobile Diary Methods in Studying Daily Family Life", 2015)
"The process of capturing events in a computer system. The result of a data collection operation is a log record. The term logging is often used as a synonym for data collection." (Ulf Larson et al, "Guidance for Selecting Data Collection Mechanisms for Intrusion Detection", 2015)
"This refers to the various approaches used to collect information." (Ken Sylvester, "Negotiating in the Leadership Zone", 2015)
"Set of techniques that allow gathering and measuring information on certain variables of interest." (Sara Eloy et al, "Digital Technologies in Architecture and Engineering: Exploring an Engaged Interaction within Curricula", 2016)
"with respect to research, data collection is the recording of data for the purposes of a study. Data collection for a study may or may not be the original recording of the data." (Meredith Zozus, "The Data Book: Collection and Management of Research Data", 2017)
"The process of retrieving data from different sources and storing them in a unique location for further use." (Deborah Agostino et al, "Social Media Data Into Performance Measurement Systems: Methodologies, Opportunities, and Risks", 2018)
"It is the process of gathering data from a variety of relevant sources in an established systematic fashion for analysis purposes." (Yassine Maleh et al, 'Strategic IT Governance and Performance Frameworks in Large Organizations", 2019)
"A process of storing and managing data." (Neha Garg & Kamlesh Sharma, "Machine Learning in Text Analysis", 2020)
"The process and techniques for collecting the information for a research project." (Tiffany J Cresswell-Yeager & Raymond J Bandlow, "Transformation of the Dissertation: From an End-of-Program Destination to a Program-Embedded Process", 2020)
"The method of collecting and evaluating data on selected variables, which helps in analyzing and answering relevant questions is known as data collection." (Hari K Kondaveeti et al, "Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture", 2021)
"Datasets are created by collecting data in different ways: from manual or automatic measurements (e.g. weather data), surveys (census data), records of decisions (budget data) or ongoing transactions (spending data), aggregation of many records (crime data), mathematical modelling (population projections), etc." (Open Data Handbook)
⛏️Data Management: Data Mapping (Definitions)
09 February 2017
⛏️Data Management: Data Discovery (Definitions)
06 February 2017
⛏️Data Management: Data Validation (Definitions)
"Evaluating and checking the accuracy, consistency, timeliness, and security of information, for example by evaluating the believability or reputation of its source." (Martin J Eppler, "Managing Information Quality" 2nd Ed., 2006)
"The process of ensuring accurate data based on data acceptance and exception handling rules." (Evan Levy & Jill Dyché, "Customer Data Integration", 2006)
"The process of ensuring that the values of data conform to specified formats and/or values." (Allen Dreibelbis et al, "Enterprise Master Data Management", 2008)
"(1) To confirm the validity of data. (2) A feature of data cleansing tools." (Danette McGilvray, "Executing Data Quality Projects", 2008)
"The act of determining that data is sound. In security, generally used in the context of validating input." (Mark S Merkow & Lakshmikanth Raghavan, "Secure and Resilient Software Development", 2010)
"Determining and confirming that something satisfies or conforms to defined rules, business rules, integrity constraints, defined standards, etc. The system cannot perform any validating unless it first has a definition of the way things should be validity The degree to which data conforms to domain values and defined business rules." (DAMA International, "The DAMA Dictionary of Data Management", 2011)
"This involves demonstrating that the conclusions that come from data analyses fulfill their intended purpose and are consistent." (Jules H Berman, "Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information", 2013)
"The act of testing a model with data that was not used in the model-fitting process." (Meta S Brown, "Data Mining For Dummies", 2014)
[data integrity validation:] "Data integrity validation allows you to verify the integrity of the data that was secured by data protection operations." (CommVault, "Documentation 11.20", 2018)
05 February 2017
⛏️Data Management: Data Stewardship (Definitions)
"Data stewardship is the function that is largely responsible for managing data as an enterprise asset. The data steward is responsible for ensuring that the data provided by the Corporate Information Factory is based on an enterprise view. An individual, a committee, or both may perform data stewardship." (Claudia Imhoff et al, "Mastering Data Warehouse Design", 2003)
"Necessary for resolving conflicts in peer-to-peer replication, data stewardship involves assigning an owner to data that resides on multiple servers and creating rules for how the data should be updated." (Sara Morganand & Tobias Thernstrom , "MCITP Self-Paced Training Kit : Designing and Optimizing Data Access by Using Microsoft SQL Server 2005 - Exam 70-442", 2007)
"An approach to data governance that formalizes accountability for managing information resources on behalf of others and in the best interests of the organization." (Danette McGilvray, "Executing Data Quality Projects", 2008)
"Deals with the ownership and accountability of data, and how people manage the data to the benefit of the organization. Data stewardship functions at two levels - Business Data Stewards deal with the higher-level metadata and governance concerns, while Operational Data Stewards focus primarily on the instances of master data in the enterprise." (Allen Dreibelbis et al, "Enterprise Master Data Management", 2008)
"This is known as a role assigned to a person that is responsible for defining and executing data governance policies." (Martin Oberhofer et al, "The Art of Enterprise Information Architecture", 2010)
"1.The formal, specifically assigned, and entrusted accountability for business (non-technical) responsibilities ensuring effective control and use of data and information resources. 2.The formal accountability for business responsibilities ensuring effective control and use of data assets. (DAMA International, "The DAMA Dictionary of Data Management" 1st Ed., 2011)
"Responsibility and accountability for the actions taken upon a defined set of data, including the definition of the consumers of the data. A data steward is not necessarily the data owner." (Craig S Mullins, "Database Administration: The Complete Guide to DBA Practices and Procedures" 2nd Ed., 2012)
04 February 2017
💠🛠️SQL Server: Administration (Killing Sessions - Killing ‘em Softly and other Snake Stories)
Introduction
There are many posts on the web advising succinctly how to resolve a blocking situation by terminating a session via kill command, though few of them warn about its use and several important aspects that need to be considered. The command is powerful and, using an old adagio, “with power comes great responsibility”, responsibility not felt when reading between the lines. The easiness with people treat the topic can be seen in questions like “is it possibly to automate terminating sessions?” or in explicit recommendations of terminating the sessions when dealing with blockings.
A session is created when a client connects to a RDBMS (Relational Database Management System) like SQL Server, being nothing but an internal logical representation of the connection. It is used further on to perform work against the database(s) via (batches of) SQL statements. Along its lifetime, a session is uniquely identified by an SPID (Server Process ID) and addresses one SQL statement at a time. Therefore, when a problem with a session occurs, it can be traced back to a query, where the actual troubleshooting needs to be performed.
Even if each session has a defined scope and memory space, and cannot interact with other sessions, sessions can block each other when attempting to use the same data resources. Thus, a blocking occurs when one session holds a lock on a specific resource and a second session attempts to acquire a conflicting lock type on the same resource. In other words, the first session blocks the second session from acquiring a resource. It’s like a drive-in to a fast-food in which autos must line up into a queue to place an order. The second auto can’t place an order until the first don’t have the order – is blocked from placing an order. The third auto must wait for the second, and so on. Similarly, sessions wait in line for a resource, fact that leads to a blocking chain, with a head (the head/lead blocking) and a tail (the sessions that follow). It’s a FIFO (first in, first out) queue and using a little imagination one can compare it metaphorically with a snake. Even if imperfect, the metaphor is appropriate for highlighting some important aspects that can be summed up as follows:
- Snakes have their roles in the ecosystem
- Not all snakes are dangerous
- Grab the snake by its head
- Killing ‘em Softly
- Search for a snake’s nest
- Snakes can kill you in sleep
- Snake taming
Warning: snakes as well blockings need to be handled by a specialist, so don’t do it by yourself unless you know what are you doing!
Snakes have their roles in the ecosystem
Snakes as middle-order predators have an important role in natural ecosystems, as they feed on prey species, whose numbers would increase exponentially if not kept under control. Fortunately, natural ecosystems have such mechanism that tend to auto-regulate themselves. Artificially built ecosystems need as well such auto-regulation mechanisms. As a series of dynamical mechanisms and components that work together toward a purpose, SQL Server is an (artificial) ecosystem that tends to auto-regulate itself. When its environment is adequately sized to handle the volume of information or data it must process then the system will behave smoothly. As soon it starts processing more data than it can handle, it starts misbehaving to the degree that one of its resources gets exhausted.
Just because a blocking occurs doesn’t mean that is a bad thing and needs to be terminated. Temporary blockings occur all the time, as unavoidable characteristic of any RDBMS with lock-based concurrency like SQL Server. They are however easier to observe in systems with heavy workload and concurrent access. The more users in the system touch the same data, the higher the chances for a block to occur. A good design database and application architecture typically minimize blockings’ occurrence and duration, making them almost unobservable. At the opposite extreme poor database design combined with poor application design can make from blockings a DBA’s nightmare. Persistent blockings can be a sign of poor database or application design or a sign that one of the environment’s limits was reached. It’s a sign that something must be done. Restarting the SQL server, terminating sessions or adding more resources have only a temporary effect. The opportunity lies typically in addressing poor database and application design issues, though this can be costlier with time.
Not all snakes are dangerous
A snake’s size is the easiest characteristic on identifying whether a snake is dangerous or not. Big snakes inspire fear for any mortal. Similarly, “big” blockings (blockings consuming an important percentage of the available resources) are dangerous and they have the potential of bringing the whole server down, eating its memory resources slowly until its life comes to a stop. It can be a slow as well a fast death.
Independently of their size, poisonous snakes are a danger for any living creature. By studying snakes’ characteristics like pupils’ shape and skin color patterns the folk devised simple general rules (with local applicability) for identifying whether snakes are poisonous or not. Thus, snakes with diamond-shaped pupils or having color patterns in which red touches yellow are likely/believed to be poisonous. By observing the behavior of blockings and learning about SQL Server’s internals one can with time understand the impact of each blocking on server’s performance.
Grab the snake by its head
Restraining a snake’s head assures that the snake is not able to bite, though it can be dangerous, as the snake might believe is dealing with a predator that is trying to hurt it, and reach accordingly. On the other side troubleshooting blockings must start with the head, the blocking session, as it’s the one which created the blocking problem in the first place.
In SQL Server sp_who and its alternative sp_who2 provide a list of all sessions, with their status, SPID and a reference with the SPID of the session blocking it. It displays thus all the blocking pairs. When one deals with a few blockings one can easily see whether the sessions form a blocking chain. Especially in environments under heavy load one can deal with a handful of blockings that make it difficult to identify all the formed blocking chains. Identifying blocking chains is necessary because by identifying and terminating directly the head blocking will often make the whole blocking chain disappear. The other sessions in the chain will perform thus their work undisturbed.
Going and terminating each blocking session in pairs as displayed in sp_who is not recommended as one terminates more sessions than needed, fact that could have unexpected repercussions. As a rule, one should restore system’s health by making minimal damage.
In many cases terminating the head session will make the blocking chain disperse, however there are cases in which the head session is replaced by other session (e.g. when the sessions involve the same or similar queries). One will need to repeat the needed steps until all blocking chain dissolve.
Killing ‘em Softly
Killing a snake, no matter how blamable the act, it is sometimes necessary. Therefore, it should be used as ultimate approach, when there is no other alternative and when needed to save one’s or others’ life. Similarly killing a session should be done only in extremis, when necessary. For example, when server’s performance has deprecated considerably affecting other users, or when the session is hanging indefinitely.
Kill command is powerful, having the power of a hammer. The problem is that when you have a hammer, every session looks like a nail. Despite all the aplomb one has when using a tool like a hammer, one needs to be careful in dealing with blockings. A blocking not addressed correspondingly can kick back, and in special cases the bite can be deadly, for system as well for one’s job. Killing the beast is the easiest approach. Kill one beast and another one will take its territory. It’s one of the laws of nature applicable also to database environments. The difference is that if one doesn’t addresses the primary cause that lead to a blocking, the same type of snake more likely will appear repeatedly.
Unfortunately, the kill command is no bulletproof for terminating a session, it may only severe the snake. As the documentation warns, there can be cases in which the method won’t have any effect on the blocking, the blocking continuing to room around. So, might be a good idea to check whether the session disappeared and keep an eye on it until it totally disappeared. Especially when dealing with a blocking chain it can happen that the head session is replaced by another session, which probably was waiting for the same resources as the previous head session. It may happen that one deals with two or more blocking chains independent from each other. Such cases appear seldom but are possible.
Killing the head session with a blocking without gathering some data provides less opportunities for learning, for understanding what’s happening in your system, of identifying what caused the blocking to occur. Therefore, before jumping to kill a session, collect the data you need for further troubleshooting.
Search for a snake’s nest
With the warning that unless one deals with small snakes, might not be advisable in searching for a snake’s nest, the idea behind this heuristic is that with a snake’s occurrence more likely there is also a nest not far away, where several other snakes might hide. Similarly, a query that causes permanent blockings might be the indication for code that generates a range of misbehaving queries. It can be same code or different pieces of code. One can attempt to improve the performance of a query that leads to blockings by adding more resources on the server or by optimizing SQL Server’s internals, though one can’t compensate for poor programming. When possible, one needs to tackle the problem at the source, otherwise performance improvements are only temporary.
Snakes can kill you in sleep
When wondering into the wild as well when having snakes as pets one must take all measures to assure that nobody’s health is endangered. Same principle should apply to databases as well, and the first line of defense resides in actively monitoring the blockings and addressing them timely as they occur. Being too confident that nothing happens and no taking the necessary precautions can prove to be a bad strategy when a problem occurs. In some situations, the damage might be acceptable in comparison with the effort and costs needed to build the monitoring infrastructure, though for critical systems it can come with important costs.
Snakes’ Taming
Having snakes as pets doesn’t seem like a good idea, and there are so many reasons why one shouldn’t do it (see PETA’s reasons)! On the other side, there are also people with uncommon hobbies, that not only limit themselves at having a snake pet, but try to tame them, to have them behave like pets. There are people who breed snakes to harness their venom for various purposes, occupation that requires handling snakes closely. There are also people who brought their relation with snakes at level of art, since ancient Egypt snake charming being a tradition in countries from Southeast Asia, Middle East, and North Africa. Even if not all snakes are tameable, snake’s taming and charming is possible. In the process the tamer must deprogram or control snakes’ behavior, following a specific methodology in a safe environment.
No matter how much one tries to avoid persistent blockings, one can learn from troubleshooting blockings, about their sources, behavior as well about own limitations. One complex blocking can be a good example with which one can test his knowledge about SQL Server internals as well about applications’ architecture. Each blocking provides a scenario in which one can learn something.
When fighting with a blocking, it’s wise to do it within a safe environment, typically a test or development environment. Fighting with it in a production environment can cause unnecessary stress and damage. So, if you don’t have a safe environment in which to carry the fight, then build one and try to keep the same essential characteristics as in production environment!
There will be also situations in which one must fight with a blocking in the production environment. Then, be careful in not damaging the data as well the environment, and take all the needed precautions!
Conclusion
The comparison between snakes and blockings might not be perfect, though hopefully it will imprint in reader’s mind the dangers of handling blockings inappropriately and increase the awareness in what concerns related topics.
⛏️Data Management: Data Matching (Definitions)
[deterministic matching:] "Deterministic matching algorithms compare and match records according to hard-coded business rules according to their precision. For instance, a rule can be set up that stipulates that every “Bill” be matched with a 'William'." (Jill Dyché & Evan Levy, "Customer Data Integration: Reaching a Single Version of the Truth", 2006)
[probabilistic matching:] "Uses statistical algorithms to deduce the best match between two records. Probabilistic matching usually tracks statistical confidence that two records refer to the same customer." (Evan Levy & Jill Dyché, "Customer Data Integration", 2006)
"A feature of data cleansing tools or the process that matches, or links, associated records through a user-defined or common algorithm." (Danette McGilvray, "Executing Data Quality Projects", 2008)
"Data-matching involves bringing together data from disparate services or sources, comparing it, and eliminating duplicate data. There are two types of algorithms that are used in data matching: (1) deterministic algorithms, which strictly use match criteria and weighting to determine the results, and (2) probabilistic algorithms, which use statistical models to adjust the matching based on the frequency of values found in the data." (Allen Dreibelbis et al, "Enterprise Master Data Management", 2008)
"A highly specialized set of technologies that allows users to derive a high-confidence value of the party identification that can be used to construct a total view of a party from multiple party records." (Alex Berson & Lawrence Dubov, "Master Data Management and Data Governance", 2010)
[deterministic matching:] "A type of matching that relies on defined patterns and rules for assigning weights and scores for determining similarity." (DAMA International, "The DAMA Dictionary of Data Management" 1st Ed., 2010)
[probabilistic matching:]"A type of matching that relies on statistical analysis of a sample data set to project results on the full data set." (DAMA International, "The DAMA Dictionary of Data Management" 1st Ed., 2010)
[fuzzy matching:] "A technique of decomposing words into component parts and comparing the parts to find an acceptable level of correspondence." (DAMA International, "The DAMA Dictionary of Data Management", 2011)
"Matching is a technique for statistical control of confounding. In the simplest form, individuals from the two study groups are paired on the basis of similar values of one or more covariates. Matching can be viewed as a special case of stratification in which each stratum consists of only two individuals." (Herbert I Weisberg, "Bias and Causation: Models and Judgment for Valid Comparisons", 2010)
"The process of comparing rows in data sets to determine which rows describe the same thing and are therefore either complimentary or redundant." (DAMA International, "The DAMA Dictionary of Data Management", 2011)
01 February 2017
⛏️Data Management: Data Strategy (Definitions)
⛏️Data Management: Data Management [DM] (Definitions)
30 January 2017
⛏️Data Management: Dirty Data (Definitions)
"Data that contain errors or cause problems when accessed and used. Some examples of dirty data are: Values in data elements that exceed a reasonable range, e.g., an employee with 4299 years of service. Values in data elements that are invalid, e.g., a value of 'X' in a gender field, where the only valid values are 'M' and 'F'. Missing values, e.g., a blank value in a gender field, where the only valid values are 'M' and 'F'. Incomplete data, e.g., a company has 10 products but data for only 8 products are included." (Margaret Y Chu, "Blissful Data ", 2004)
"Data that contain inaccuracies and/or inconsistencies." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management" 9th Ed., 2011)
"Poor quality data." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed, 2011)
"Data that is incorrect, out-of-date, redundant, incomplete, or formatted incorrectly." (Craig S Mullins, "Database Administration", 2012)
"Data with inaccuracies and potential errors." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015)
29 January 2017
⛏️Data Management: Data Dictionary (Definitions)
About Me
- Adrian
- Koeln, NRW, Germany
- IT Professional with more than 24 years experience in IT in the area of full life-cycle of Web/Desktop/Database Applications Development, Software Engineering, Consultancy, Data Management, Data Quality, Data Migrations, Reporting, ERP implementations & support, Team/Project/IT Management, etc.