7 Best data management tools in 2021

Data is produced and consumed at volumes and speeds which were unimaginable just a decade ago. Top players have taken advantage of this growth. Tapping into data resources for actionable insights — aptly called the new oil — makes data-driven companies dominate their competition.

But the proliferation of data can lead to growing pains. Companies find themselves increasingly incapacitated by the vast and messy nature of their in-house data. The larger your dataset, the more difficult it becomes to sift through, organize, and analyze data to gain valuable insights.

Say hello to data management tools: built to tackle the data challenges of tomorrow that you’re facing today.

What are data management tools?

Data management tools are software solutions that help you to manage data assets throughout their lifecycle, from creation to retirement.

The selling proposition is that data management software assists you with automating, speeding up, or improving the structure of the main management processes surrounding data. For example:

1. Data access — provision granular access to your data resources via tooling. This guarantees unhinged access to your data scientists, analysts, and data engineers, while also keeping it secure and locked away from prying eyes.
2. Data quality — ensure that data is of high quality end-to-end.
3. Data preparation — wrangle data into the desired shape for analytics, science, and visualization.
4. Data integration — combine different data sources into databases and data warehouses.
5. Data governance — apply business and regulatory rules to your data to comply with legal aspects and operational constraints.
6. Master data management (MDM) — centralize the definitions of metadata and data assets across your entire organization for data stewardship.
7. Reference data management (RDM) — specify permissible values that can be used by other fields and data.
8. Data streaming — filter, clean, and analyze data in real-time while it’s streaming, not just when it’s stationary.
9. Data visualization — accelerate dashboard construction and sharing via tooling.
10. Machine learning (ML) — unlock the power of artificial intelligence with tooling that speeds up ML projects.

The best data management tools of 2021

Without further ado, here are the best data management tools in 2021. To make it easier for you we split them into high-engineering cloud solutions, which benefit advanced engineers and researchers, and high-usability cloud solutions which are best for operatives and businesses wanting to take the market fast.

High-Usability Cloud Solutions

Though the high-usability tools are often built upon the cloud technologies of AWS, Azure, or GCP, these specific data management tools additionally built several applications and solutions to make working with data easier for the data professionals. Instead of writing code for every solution from scratch, you can just tap into a premade configuration or mini-application to do the work for you.

Keboola

Keboola is a web-based end-to-end data operations platform. It started as a data integrator, allowing you to automate your ETL pipelines, but has since evolved to offer:

• Infrastructure as a service — build your ETL pipelines through a low-code/no-code user interface or via code. One-click deployments allow both technical and nontechnical professionals to construct and work on data applications collaboratively.
• End-to-end Automation. Automate all parts of your data architecture: extract data from a variety of sources (SQL databases, NoSQL databases, data warehouses, and Third-Party Apps), load data into your preferred destination data storage, customize the necessary transformations to clean and wrangle your data sets, and deploy it into your data visualization tool of choice (for example Looker BI or Tableau). Automate the end-to-end process via orchestrations, which schedule the needed job to always have fresh data, and automatically monitor the entire pipeline for errors.
• MDM solutions by tracking data lineage and operational metadata and the Data Catalog functionality for metadata management and unified definitions.
• Data governance with extensive tracking of jobs, that allow tracing on the transaction and events levels, as well as audit capabilities and fingerprinting to comply with regulatory standards.
• Business intelligence and Machine Learning pipelines — with data sharing, devoted data science and analytic workspaces for collaboration, and out-of-the-box tools such as Jupyter Notebooks and Google machine learning APIs, Keboola offers a myriad of tools to help the last-mile analytic experts drive valuable insights.

Curious about how Keboola can speed up your data projects? We offer a pay-as-you-go model, with a generous always-free monthly service so you can try Keboola for yourself without commitments.

2. Talend

Talend is a platform that unites data integration and governance. In its toolbox you can find:

1. Data Integration to link a myriad of source systems using standard connections.
2. Extensive Master Data Management functionalities.
3. Pipeline Designer, a GUI based tool to map out your ETL pipeline.
4. Data Inventory (similar to Data Catalogs of other providers) to discover, search, and share data across Talend Data Fabric.
5. Stitch, a self-service interface to move data rapidly from its sources to the cloud warehouse.

Unfortunately, Talend seems to be still ironing over some of its usability features and lacks speedy support resolutions.

3. Fivetran

Fivetran is a data integration platform, whose main focus is to connect data from its sources to their destination storage with as little code as possible.

Fivetran extracts data with pre-built connectors, which you activate via a user interface. In the normalization layer or within the destination storage, you can clean and wrangle your data to implement the necessary transformations.

Fivetran offers some automation with incremental batch updates and automated schema migrations.

Though its selling proposition is user-friendliness, it lacks a bit in the last-mile department, offering less tooling for data science and machine learning than the other vendors.

4. Panoply

Panoply is another data integration platform, which uses cloud-based technologies to connect your sources to their destination storage.

An advantage of Panoply is its user-friendliness and that it bolsters over 180 sources and connects to a multitude of analytic tools (such as Metabase, Looker, Mode Analytics, Tableau, Power BI, …) and marketing apps.

A disadvantage of Panoply is its steep pricing curve.

High-Engineering Cloud Solutions

High-engineering cloud solutions are data management tools that open-up all the capabilities of data management from the bare-metal up with the advantage of being deployed in the cloud. There are three main contenders which are dominating the field: AWS, Azure, and GCP.

5. AWS

AWS is a data platform offering multiple cloud services. As with any cloud-native (vs. on-premise) solution, there are many benefits associated with the cloud: easy scaling by adding new resources, automated backups and restores, resiliency, and a platform of applications, which can be incorporated into your workflow.

For the data practitioner, AWS offers multiple data management tools:

1. Scalable S3 or EC2 storage.
2. Relational databases and warehouses such as Amazon RDS, Amazon Aurora, and Amazon Redshift.
3. NoSQL databases such as Amazon Neptune (graph), Amazon Timestream (time series), Amazon DynamoDB (key-value), etc.
4. Newly introduced connectors for Graphana and Prometheus to monitor your deployment.
5. AWS Glue with Data Catalog to find and access data, DataBrew to enrich data, and Elastic Views to use SQL for manipulating that data.
6. Amazon SageMaker to build, train, and deploy ML models.

AWS also prepackages its services for industry-specific use cases. For example, retailers can tap into AWS to comprehensively collect and analyze data from the customer journey.

6. Microsoft Azure

Microsoft Azure offers on-premise, hybrid, or multi-cloud solutions for data challenges.

Among the data management tools they offer, you can find:

1. Standard SQL databases (Azure Database for MySQL, Azure Database for PostgreSQL, Azure SQL, …), Microsoft SQL Server on virtual machines, Blob storage, Azure Data Lake Storage, …
2. Azure Data Share, a service to share enterprise data with external organizations.
3. Data Catalog to organize, standardize, and easily find your data.
4. Event Hubs to unify telemetry from millions of devices. Especially useful if you are looking for an Internet-of-Things (IoT) solution.
5. Devoted tools for high-end artificial intelligence projects, such as Anomaly Detectors, Azure Databricks to spin an Apache Spark-based analytic platform, Computer Vision, and other specialized software.

7. Google Cloud Platform

The Google Cloud Platform (GCP) is another contender for all-in-cloud data platforms. Though less popular than the AWS and Azure alternatives, GCP offers a suite of tools for data management with a high emphasis on big data management:

1. Cloud SQL databases for the classical MySQL, PostgreSQL, and SQL Server relational databases.
2. Firebase Realtime Database to store and sync data in real-time.
3. Firestore, a Cloud-native NoSQL to easily develop rich mobile, web, and IoT applications.
4. Cloud Bigtable, a cloud-native NoSQL wide-column store for large scale, low-latency workloads.
5. DataFlow for streaming analytics.
6. BigQuery the famous columnar data warehouse for big data analytics.
7. BigQueryML allows data scientists to build and operationalize ML models on a high-scale.
8. Looker, a platform for business intelligence and embedded analytics.
9. Data Catalog, a metadata service for discovering, understanding, and managing data.
10. Dataprep, a service to speed up data preparation for analysis and machine learning.

How to pick the best tool for your organization?

1. What is the tool for? You need to have a clear idea of the problem you are trying to solve. Having a clear data strategy can help you navigate the translation of business objectives into data objectives. If you need to build a rocketship from scratch, hire NASA’s engineers and opt for AWS. If you need to move to market as quickly as possible, opt for high-usability tools to speed up your time to insights.
2. Who is the tool for? In other words, who will be using the tool? If your team is composed of non-technical people, pick the data management software which offers user interfaces for building blocks. If your tool is composed of engineers, they might or might not be qualified to work with the big tech solutions (AWS, GCP, …). Keep in mind that mixed teams need to have no-code, low-code and full-code capabilities to be fully empowered.
3. What is the total cost of ownership (TCO)? The total cost of ownership includes everything. The obvious cost is the pricing of the tool. But it also includes other hidden costs. For example, how expensive is it to try a tool out (initial charges, setup charges, migration costs), how expensive is maintenance, and once things go wrong — and they will — how extensive is the tool’s support to help you untangle your problem as fast as possible.

When it comes to data management tools, you need to apply the same considerations as with other IT procurement projects. Ask yourself:

At Keboola we take pride in the data management tools we built because we live and breathe data operations ourselves. This is why we designed tooling which is accessible to both technical and non-technical professionals, can scale with your needs, and has one of the lowest TCOs out of all of the tools ( our customers love our support service!).

We offer a no-questions-asked free tier. Try Keboola out and check for yourself what Keboola can do for you.

Originally published at https://www.keboola.com.