Top 10 Data Engineering Tools to Learn and Land a High-Paying Job
Given the enormity of data and the complexity of applications, resorting to manual engineering rarely helps
The primary role of data engineers comprises transforming data into useful information. However, given the enormity of data and the complexity of applications, resorting to manual engineering for developing complex models rarely helps. Data engineers can rely on a variety of data engineering tools to simplify and automate certain tasks such as building data pipelines and developing working algorithms. The range of tools available in the market can be quite overwhelming, here, we provide you with a list of the top 10 data engineering tools you can have in the data engineering stack.
1. Apache Kafka:
Apache Kafka is the tool mostly applied to process and build real-time data pipelines. It can handle massive volumes of data streaming continuously including analysing website activity, gathering metrics, and monitoring log files. Though many learners consider it a difficult-to-learn application, it is used by more than 50% of Fortune 500 companies.
2. Apache Airflow:
A highly effective tool for tasks that involve managing complex workflows, it is available as an open-source data engineering tool. It is extremely easy to use and is supported by a huge community for instant upgrades. Used by around 8,000 companies, there is the least chance of it being replaced in near future.
3. Cloudera Data:
It is a dual-mode Cloud Data management platform mostly used for machine learning and data analytics tasks. Because of its dual nature, it has become popular among large-scale companies. With a user-friendly interface and plenitude of tutorials and documentation, it is the most widely used big data engineering tool in the financial sector.
4. Apache Hadoop:
Apache Hadoop is not just one tool but a combination which are used for quick data gathering in an orderly manner, performing real-time data processing, and generating easy to understanding analytics. These reasons should be enough to understand why it is a kind of household name in many industries. To learn this tool, you need to have preliminary knowledge of SQL. Once this hurdle is crossed, it is pretty much a cakewalk.
5. Apache Spark:
An open-source data engineering and analytics tool is compatible with programming languages like Python, Java, and Scala. With the ability to queue more than 100 tasks in memory, it is the fastest data management tool you can have in the stack. Using Apache Spark is easy as long as the workflow is kept simple, as the complexity increases, more layers are added to the Spark application.
6. Amazon Redshift:
It is AWS-based data management and warehousing tool, primarily used for segmenting datasets, analysing trends and anomalies to generate gainful insights. It depends on PostgreSQL 8, for fast performance and efficient querying. With Redshift, it is easy to source data in real-time as it is a column-oriented database, designed to run on SQL and hence is used by many companies for data migration tasks also.
7. Apache Cassandra:
An open-source, scalable, and NoSQL database platform, used by the largest number of users on the Internet. It is a masterless architecture that comes with zero downtime, zero lock-ins, and has a global scale for data sovereignty. It is popular with many organisations for the speed and simplicity it offers even for operations involving continuous data crunching.
8. Apache Kudu:
It is an Apache Hadoop ecosystem-compatible application that is open-sourced and has internally organized data structures. It has column-oriented data storage system, and therefore it is capable of fast analytics. As it cannot support foreign keys and multi-row transactions, it is considered a dying tool. But if you are looking for a tool for short-term usage, Kudu can be one as it is easy to break into.
9. Apache Hive:
An extension of Apache Hadoop, it provides data warehouse services using centrally stored information to arrive at informed and data-driven decisions. Using Hive, users can read, write and manage petabytes of data using SQL. It can query large datasets, using Apache Tez or MapReduce, with a SQL-like interface. Very popular in the retailing industry, it can efficiently store and keep track of data stored at multiple locations.
10. Apache Turbine:
A great data engineering tool to take a resort at, if the user experience and web development is the prime concern. As it is a Java-based application, it is most amenable to experienced developers. It makes for an excellent choice for applications-oriented towards service-based architectures. Tribune supports services in a variety of areas such as security management systems, scheduling services, XML-defined form validation services, XML-RPC services, etc.
The post Top 10 Data Engineering Tools to Learn and Land a High-Paying Job appeared first on .
Given the enormity of data and the complexity of applications, resorting to manual engineering rarely helps
The primary role of data engineers comprises transforming data into useful information. However, given the enormity of data and the complexity of applications, resorting to manual engineering for developing complex models rarely helps. Data engineers can rely on a variety of data engineering tools to simplify and automate certain tasks such as building data pipelines and developing working algorithms. The range of tools available in the market can be quite overwhelming, here, we provide you with a list of the top 10 data engineering tools you can have in the data engineering stack.
1. Apache Kafka:
Apache Kafka is the tool mostly applied to process and build real-time data pipelines. It can handle massive volumes of data streaming continuously including analysing website activity, gathering metrics, and monitoring log files. Though many learners consider it a difficult-to-learn application, it is used by more than 50% of Fortune 500 companies.
2. Apache Airflow:
A highly effective tool for tasks that involve managing complex workflows, it is available as an open-source data engineering tool. It is extremely easy to use and is supported by a huge community for instant upgrades. Used by around 8,000 companies, there is the least chance of it being replaced in near future.
3. Cloudera Data:
It is a dual-mode Cloud Data management platform mostly used for machine learning and data analytics tasks. Because of its dual nature, it has become popular among large-scale companies. With a user-friendly interface and plenitude of tutorials and documentation, it is the most widely used big data engineering tool in the financial sector.
4. Apache Hadoop:
Apache Hadoop is not just one tool but a combination which are used for quick data gathering in an orderly manner, performing real-time data processing, and generating easy to understanding analytics. These reasons should be enough to understand why it is a kind of household name in many industries. To learn this tool, you need to have preliminary knowledge of SQL. Once this hurdle is crossed, it is pretty much a cakewalk.
5. Apache Spark:
An open-source data engineering and analytics tool is compatible with programming languages like Python, Java, and Scala. With the ability to queue more than 100 tasks in memory, it is the fastest data management tool you can have in the stack. Using Apache Spark is easy as long as the workflow is kept simple, as the complexity increases, more layers are added to the Spark application.
6. Amazon Redshift:
It is AWS-based data management and warehousing tool, primarily used for segmenting datasets, analysing trends and anomalies to generate gainful insights. It depends on PostgreSQL 8, for fast performance and efficient querying. With Redshift, it is easy to source data in real-time as it is a column-oriented database, designed to run on SQL and hence is used by many companies for data migration tasks also.
7. Apache Cassandra:
An open-source, scalable, and NoSQL database platform, used by the largest number of users on the Internet. It is a masterless architecture that comes with zero downtime, zero lock-ins, and has a global scale for data sovereignty. It is popular with many organisations for the speed and simplicity it offers even for operations involving continuous data crunching.
8. Apache Kudu:
It is an Apache Hadoop ecosystem-compatible application that is open-sourced and has internally organized data structures. It has column-oriented data storage system, and therefore it is capable of fast analytics. As it cannot support foreign keys and multi-row transactions, it is considered a dying tool. But if you are looking for a tool for short-term usage, Kudu can be one as it is easy to break into.
9. Apache Hive:
An extension of Apache Hadoop, it provides data warehouse services using centrally stored information to arrive at informed and data-driven decisions. Using Hive, users can read, write and manage petabytes of data using SQL. It can query large datasets, using Apache Tez or MapReduce, with a SQL-like interface. Very popular in the retailing industry, it can efficiently store and keep track of data stored at multiple locations.
10. Apache Turbine:
A great data engineering tool to take a resort at, if the user experience and web development is the prime concern. As it is a Java-based application, it is most amenable to experienced developers. It makes for an excellent choice for applications-oriented towards service-based architectures. Tribune supports services in a variety of areas such as security management systems, scheduling services, XML-defined form validation services, XML-RPC services, etc.
The post Top 10 Data Engineering Tools to Learn and Land a High-Paying Job appeared first on .
Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks belong to their rightful owners, all materials to their authors. If you are the owner of the content and do not want us to publish your materials, please contact us by email – [email protected]. The content will be deleted within 24 hours.