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What is the difference between SQL and T-SQL?
SQL is the language used to query databases. You run a query, such as SELECT or INSERT, and get a result. T-SQL stands for Transact-SQL and is a language and set of extensions for SQL Server that allows for further programming logic to be used with SQL.
SQL is the language used to query databases. You run a query, such as SELECT or INSERT, and get a result.
T-SQL stands for Transact-SQL and is a language and set of extensions for SQL Server that allows for further programming logic to be used with SQL.
See lessWhat is the difference between SQL and PL/SQL?
SQL is the language used to query databases. You run a query, such as SELECT or INSERT, and get a result. PL/SQL stands for Procedural Language/Structured Query Language. It’s Oracle’s procedural language and is built on top of SQL. It allows for more programming logic to be used along with SQL.
SQL is the language used to query databases. You run a query, such as SELECT or INSERT, and get a result.
PL/SQL stands for Procedural Language/Structured Query Language. It’s Oracle’s procedural language and is built on top of SQL. It allows for more programming logic to be used along with SQL.
See lessWhat is the difference between SQL, Oracle, MySQL, and SQL Server?
SQL is the name of the language used to query databases and follows a standard. Oracle, MySQL, and SQL servers are different implementations or versions of a database management system, which implement the SQL standard and build on it in different ways. Oracle database is targeted at large companiesRead more
SQL is the name of the language used to query databases and follows a standard. Oracle, MySQL, and SQL servers are different implementations or versions of a database management system, which implement the SQL standard and build on it in different ways.
See lessOracle database is targeted at large companies, SQL Server is owned by Microsoft, and MySQL is owned by Oracle but targeted toward smaller companies and systems.
What Skills Does a Data Engineer Need?
Here are some common skills and expertise you’ll need in a data engineering role: Knowledge of database tools Coding Critical thinking Experience with data analysis Knowledge of data transformation, buffering, ingestion, and mining tools AI and machine learning experience Data warehousing and ETL toRead more
Here are some common skills and expertise you’ll need in a data engineering role:
- Knowledge of database tools
- Coding
- Critical thinking
- Experience with data analysis
- Knowledge of data transformation, buffering, ingestion, and mining tools
- AI and machine learning experience
- Data warehousing and ETL tools
- Real-time processing frameworks
See lessMention some of Hadoop’s key attributes.
Hadoop is a free, open-source framework whose code can be modified to suit different needs. It supports faster-distributed data processing with MapReduce. Hadoop is quite forgiving and, by default, permits the user to build three clones of each block at several nodes. Therefore, even if one of the nRead more
- Hadoop is a free, open-source framework whose code can be modified to suit different needs.
- It supports faster-distributed data processing with MapReduce.
- Hadoop is quite forgiving and, by default, permits the user to build three clones of each block at several nodes. Therefore, even if one of the nodes fails, we may still recover the data from another node.
- Scalable and hardware-neutral.
- Due to Hadoop’s cluster-based data storage, all other operations were unaffected. Thus, it is trustworthy. The failure of the machines has no impact on the data that has been stored.
See lessDescribe the fundamental idea underlying the Apache Hadoop Framework.
It is based on the MapReduce algorithm, to be precise. The Map and Reduce procedures of this technique are used to process a large data set. Reduce summaries of the data while Map filters and sorts the data. The main ideas behind this paradigm are scalability and fault tolerance. By effectively utilRead more
It is based on the MapReduce algorithm, to be precise. The Map and Reduce procedures of this technique are used to process a large data set. Reduce summaries of the data while Map filters and sorts the data. The main ideas behind this paradigm are scalability and fault tolerance. By effectively utilizing MapReduce and Multi-threading, we may successfully implement these functionalities in Apache Hadoop.
See lessHow does orchestration work?
IT firms must manage a lot of servers and apps, but doing it manually isn’t scalable. The more complicated an IT system is, the harder it is to keep track of all the moving elements. The demand to integrate several automated jobs and their configurations across groups of systems or machines is growiRead more
IT firms must manage a lot of servers and apps, but doing it manually isn’t scalable. The more complicated an IT system is, the harder it is to keep track of all the moving elements. The demand to integrate several automated jobs and their configurations across groups of systems or machines is growing, coupled with the necessity to combine such automated operations and settings. This circumstance benefits from the usage of orchestration.
A computer system, application, and service orchestration is the automated configuration, administration, and coordination of these components. Orchestration makes it easier for IT to manage challenging operations and processes. Numerous technologies for container orchestration, including Kubernetes and OpenShift, are available.
See lessHow does schema evolution work?
Schemas have advanced to the point where the same set of data can be stored in numerous files with different but compatible schemas. You can automatically identify and combine those files’ schema by using Spark's Parquet data source. A typical approach to dealing with schema evolution without automaRead more
Schemas have advanced to the point where the same set of data can be stored in numerous files with different but compatible schemas. You can automatically identify and combine those files’ schema by using Spark’s Parquet data source.
A typical approach to dealing with schema evolution without automatic schema merging is to reload historical data, which is time-consuming.
See lessWhich two messages does NameNode get from DataNode?
DataNodes provide NameNodes with information about the data in the form of messages or signals. The two indicators are: Block report signals, which is a list of the data blocks stored on the DataNode and an explanation of how they operate. DataNode's heartbeat, which indicates it’s active and workinRead more
DataNodes provide NameNodes with information about the data in the form of messages or signals.
The two indicators are:
- Block report signals, which is a list of the data blocks stored on the DataNode and an explanation of how they operate.
- DataNode’s heartbeat, which indicates it’s active and working. A recurring report helps decide whether to employ NameNode. If this signal is not sent, DataNode’s operation has apparently ceased.
See lessAs a data engineer, how would you go about creating a new analytical product?
Understanding the overall product outline will help you fully grasp a project’s requirements and scope. The second stage would be to research each measure’s specifics and causes. Consider as many potential problems as you can to build a more resilient system with an appropriate level of granularity.
Understanding the overall product outline will help you fully grasp a project’s requirements and scope. The second stage would be to research each measure’s specifics and causes.
Consider as many potential problems as you can to build a more resilient system with an appropriate level of granularity.
See less