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We have already discussed in detail what data management systems are and why are they important to businesses. Data management is essential to run an organization, and for that we require specific data processing tools for specific types of data management. In this blog, we will discuss these tools and how they help in data management. There are numerous data management techniques and tools available, but we will cover some of the best and most trusted ones.
Assist in maintaining and visualization of master data of the whole enterprise and encourage data stewardship by the help of specialists who run analyse the data.
It’s an exceptionally comprehensive master data management tool with an ML – centric interface for data management. It also helps in data processing and transformation with an automated metadata harvesting as well as project configuration. It supports all the data that there is while being multi-domain in its functioning.
Providing modern solutions to master data management while leveraging AI-ML, informatica lets users create, locate, or access the verified data whenever required. Some of Informatica MDM features also include data security, data integration, data quality, and management of business processes.
Profisee is an extremely pliable tool used for data management, which lets its users model the data in its exact state without manipulating it. It comes with data governance, data stewardship, data analysis, batch integration, golden data management (normalizing and cleansing data), hierarchy management and data quality. It can be delivered in-cloud or on-prem.
Being a recent addition in the industry, it is not dependent on Hadoop, already optimized to support Oracle, consists of a user interface that can be modified bit by bit as per the business role preferences such as analysts, engineers, operators etc. Data management is done using the metadata that follows the updates and changes in the system.
Deployment can be done on-prem or in cloud and lets the users in the consolidation of master data. Supports all domains and retrieves master data from the SAP apps automatically. It is used to load master data from different sources, distribute master data to prospects, and allow its users to govern the master data. Master data governance framework that will enable you to characterize, approve, and screen your organization rules to analyze data management activities.
Since data storage has become cost-effective and less expensive, innovative data management solutions are coming up our way. Organizations with vast information to store, filter through and manage their data wholly in the cloud. Cloud data management tools have made it possible to manage and maintain in the cloud in its entirety. Many such tools for cloud data management available are below:
Offering Amazon Redshift, which analyzes data using the pre-existing software for analytics in an organization, Amazon Web Services provides tools which help in a successful cloud data management stack. Some of the main characteristics include Amazon S3 which is used for temporary as well as intermediate storage, Amazon Glacier helps in long-term backup as well as storage, AWS Glue is used for creating data catalogs to categorize, search and query the data of an organization, Amazon Athena is used for data analytics (SQL-based), Amazon Quicksight for constructing dashboards and data visualization.
Microsoft’s Azure provides various approaches to set up a cloud-based data management system and analytics tool which can then be utilized in the Azure-stored data. Similarly, like AWS, Azure also lets more than one database/data warehouse pair with different tools for data management. Product services include SQL databases (standard) and SQL servers (VM-based), Blob storage, NoSQL-type options for storing tables, deployments of private cloud, Azure Data Explorer (ADX) – helps in real-time analysis of large data sets with no requirement of preprocessing, integration for ELT/ETL services with Panoply.
Similar to Amazon, Google Cloud offers a wide range of tools for cloud-based data management. Major components such as BigQuery for storing tables, Cloud BigTable for NoSQL type database storage, Cloud Pub/Sub & Cloud Data Transfer is used for data intake, helps find different sources of data, Big Query analytics is used for SQL-type queries, ML is used for advanced analysis, Data Studio and Cloud Datalab are included in it.
Some of the most used ETL (Extract, Transform and Load) and data integration tools are:
Being an on-premise ETL tool, it provides seamless connectivity and integration with different kinds of data sources with the help of out-of-the-box connectors, automated data validation, advanced data transformations, metadata-driven management.
Being a cloud-based ETL platform, it comes with pre-integrated data sources on and off the cloud, transfers data into Amazon Redshift, S3, Big Query, Panoply, PostgreSQL, schedules data replication, handles errors and alerts whenever possible.
Fivetran includes a data pipeline that comes with an interface that helps in integrating the data from various databases into one particular data warehouse. Its primary features are providing direct integration and sending data to a secure connection with the help of a caching layer that moves the data from one place to the other without storing any copy on its application server. There are no data limits and is useful for data centralization of an organization to determine Key Performance Indicators (KPIs) across a whole enterprise.
Microsoft comes with SSIS which is a graphical interface used to manage ETL with the help of MS SQL Server. It is a database management tool and its easy-to-use interface lets its users have access to data warehousing solutions with no requirement of coding. It also comes with graphical interface that aids in easy drag-and-drop ETL in order to use more than one data type. MySQL is also one of the best data management tools.
Another Microsoft product, Azure Data Factory is an ETL tool used for building ETL pipelines in a graphical interface with very less requirement for coding. Wide variety of data connectors for easy data ingestion. Data is loaded into Azure Data Warehouse very conveniently.
Data integration, cleansing, masking, and profiling is done using this tool. It is one of the most common data quality management tools. Primary features of the tool consist of Master Data Management (MDM) functionality, management of many sources with the help of GUI, data of the organization can be seen accurately.
Data transformation tools make changes or transformations in the data sets to create value out of the data and make it readable for users. Some of the most useful tools data transformation tools include:
Dataform manages cloud data warehouse processes. It’s SQL-based platform with major components being writing workflows in SQL as a collaborative team, writing data quality tests and setting alerts if the data is not from a trusted source, create a repository which is centralized to define data throughout the organization and documenting the data as well as discovering new datasets in the data catalog.
Data Build Tool being a data transformation tool (SQL based) lets the user to do modular transformation flows directly by the command line. It is designed with a built-in eye for streamlining data analytics as well as engineering workflows.
Recent addition as an open-source data infrastructure tool, Airflow helps create, schedule, and monitor ETL processes with the help of python. Prominent features include DAGs that help spread tasks of the scheduler to other workers without defining any parent-child relationship amidst data flow stack. Easy user interface that lets you manage your DAGs (Directed Acyclic Graphs), is extremely extensible & scalable.
Developed by Spotify, this tool was intended to ease the management of batch processes that were running for a long time so that it deals with the processes that are way past the extent of ETL. Data pipelines are built efficiently using python, consisting of an interface that lets you visualize your tasks for workflow management. Atomic file systems operation doesn’t let the pipelines clash with partial data.
Define reference data and various other business-related procedures, RDM tools help in collecting and managing reference data. Some of the most commonly used RDM tools include:
Being a reference data management tool, it helps in automating the workflows for creating fresh. Helps in delivering codes to the users in a familiar way. Data mapping is done accurately in order to access data without any blockade. Also aids in comparing data of the whole company.
Available as a multi-domain tool for modeling, it helps in structuring different codes to different paths, providing businesses with automation, governance as well as control over various objects of data reference. Data is also a key feature of this tool.
This tool is a graph-based tool with a user-friendly interface and is designed on graph databases to achieve pliability while characterizing relationships as well as while scaling different data stores. Integrating with various sources of data and MDM tools become easier.
In order to analyze, visualize and explore large data sets, designing reports and creating dashboards, data analytics and data visualization tools are used. Some of the most commonly used tools are:
A business intelligence platform, tableau can be accessed on cloud or as a software. This is one of the most common big data warehouse or management tools that provides hassle free connection to different sources of data. Everyone in the organization can access the visualizations easily. Consists of maps and comprehensive dashboards to explore data.
Chartio is a platform for visualization and business intelligence and has two modes: Interactive mode (where the data is dropped and dragged to design dashboards as well as to filter and share them) and SQL mode (where the insights from the data are extracted). Data layering and data blending are other key features of this tool.
Metrics are defined using LookML in this tool and the queries in SQL are written to justify anything about these metrics. Looker being a data modelling language it helps in making simple dashboards that easier to understand. Provides a free access to reports and dashboards for all employees of the organization.
Being a data visualization tool, it provides a friendly user interface with numerous data connectors. Comes with data auditing, management, and easy accessibility for the users. Provides users with different options of data visualizations.
Microsoft’s Power BI is an analytics tool which comes with a built-in library of connectors. The interface is designed in such a way that it resembles MS excel to make it user friendly. Larger volumes of data may cause lagging.
While there are many enterprise data management tools out there, we have curated a list of the best data management software that are most commonly used and popular ones. In today’s day and age, where data is the hot topic, everyone looks for management tools that are low on cost and high on efficiency. These tools are divided into six categories, namely; Master data management tools, Cloud data management tools, ETL and data integration tools, Data transformation tools, Reference data management tools and data analytics and data visualization tools.