Database management is a very challenging and ambiguous task to accomplish. As the experts point out, it includes developing data architectures, defining data management practices and policies, and managing the data lifecycle in an organization. However, when people refer to data management, what does it mean? Let us explore.

Cloud data management

This is a new-age concept of integrating the data from an organization’s internal ecosystem to a cloud environment. Data management’s major advantage in the cloud is that the intake, storage, and processing of data everything takes place on the cloud-based medium.

Data integration / ETL

This is the upload of data from its various sources to the data warehouse by summarizing and aggregating the data into a format to make it suitable for in-depth analysis.

Master data management

This is the mode of managing the most relevant and critical data related to an organization’s management, including the accounts, customers, and data related to business transactions. This should be done in a very structured and organized way to prevent redundancy and errors.

Management of reference data

It defines permissible values that can be further used by the other data fields like the zip codes, cities, countries, product codes, etc. The reference data can be custom made or provided externally.

Data analytics and visualization

This includes processing the data from big data sources and data warehouses and executing advanced data analytics. Advanced analytics also allow analysts and scientists to present the data insights as visualization.

As data volume and diversity are increasing massively lately, you may need high-quality tools to do data management and analysis effectively. Business enterprises use advanced data management tools that enable the objectives to discuss above. Let us explore the major categories of data management tools below. To identify the tools apt for your purpose, you may consult with expert database administration services like RemoteDBA.com.

Cloud tools

As we have seen above, cloud data management is a fundamental requirement for organizations to cut costs and ensure availability. The cloud data tools will help connect to various data sources and integrate those through APIs and direct DB connections.

ETL tools

These tools will help organizations to load different types of data from various sources. They can also help define more complex and automated data transformations, test data pipelines, and load data continuously to the target databases and data warehouses.

Transformation tools

This set of tools will transform raw data into much cleaner and easily analyzable sets of data as it moves from various individual data sources to external warehouses or internal warehouses.

MDM tools

MDM or Master Data Management tools will help visualize even the most complex sets of data across the organization and facilitate data stewardship by the subject matter experts who can oversee the creation, management, and maintenance of data.

RDM tools

RDM or Reference Data Management tools often come as a sub-category of the MDM tools, which help define the business processes centered on the reference data and help the stakeholders populate the reference data and handle it easily effectively overtime.

Data analytics and visualization tools

Analytics tools are essential for modern-day organizations, helping the users explore and visualize huge sets of data and generate easily understandable dashboards and reports and extract actional insights for better business decisions.

Let us further explore some of the best data management tools available.

Panoply

It is primarily a cloud-native data warehouse that helps integrate and manage the organizational data in a much easier way. Panoply offers a big set of native data connectors for easy ingestion of data. It also features an intuitive dashboard to manage the data and help take the guesswork out.

AWS

Amazon Web Services or AWS offers an advanced set of tools that come together to a comprehensive data management suite. Amazon S3 is for intermediate storage, whereas Amazon Glacier offers long-term storage and backup. AWS Glue help in preparing data catalogs and to search and query the data. Amazon Athena is for SQL-based Analytics, and Amazon Redshift enables data warehousing.

Microsoft Azure

Azure is an advanced platform for data management, which puts forth several different tools and customized ways for cloud data management. There are many analytics tools, too, which can be used on the Azure-stored data for effective visualization of data. As with the AWS suite, Azure also enables various data warehouse styles with a greater set of custom tools. There are features like standard SQL databases, blob storage, table storage like NoSQL, private cloud, etc.

Google Cloud

Similar to AWS, the cloud services of Google also put forth a wider set of tools for data management as well as a better workflow manager, which can be used to tie up various components together. The major Google cloud components are tabular data storage with BigQuery, Cloud BigTable, BigQuery analytics, Cloud Datalab for data science, etc.

Informatica Powercenter

It is an ETL tool, which features seamless integration and connectivity with various data sources and automated data validation. It also offers advanced features for data transformations and can also support non-relational data along with parsing of JSON, XML, PDF, and MS Office data.

Stitch Data

This is another cloud-based platform for ETL featuring pre-integration with various data sources on and off the cloud, which can move data into various platforms like Amazon Redshift, PostgreSQL, BigQuery, Panoply, S3, etc. Stitch Data enable easy data replication scheduling and is also capable of better error handling and automated resolutions.

Fivetran

It is a managed data pipeline offering an easy-to-handle web interface to integrate SaaS data into a single data warehouse. Fivetran offers direct integration of data over a secure connection, and the caching layer enables data movement without storing a copy on the application server. There is no data limit on Fivetran.

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Some other advanced data management and analytics tools including but not limited to Blendo, Microsoft SQL Server SSIS, Azure Data Factory, Talend, Alooma, Dataform, DBT (Data Build Tool), Airflow, Luigi, Dell Boomi, Profisee, SAP NetWeaver, Semarchy xDM, Tibco MDM, Ataccama ONE, Stibo STEP, Collibra, Magnitude, Informatica MDM Reference 360, Reltio Cloud, Tableau, Chartio, Looker BI, Metabase, etc.