16 Best Data Integration Tools to Accelerate Marketing Analysis

A recent study by Impact revealed that data-driven organizations are 23 times more likely to gain new customers, 6 times more likely to maintain a relationship with them, and 19 times more likely to be profitable. This goes to show that data now controls the world of modern enterprise.

In this era of Big Data, businesses at all levels are exposed to an ever-increasing volume of data streams from a wide array of sources. These sets of data are mission-critical in helping them optimize their marketing strategies. However, to make the best use of data for productive decisions, businesses need to pull said data from all available sources and consolidate them in one destination for optimal analytics and data management.

This is where data ingestion tools come in.

In today’s guide, we will explain what data ingestion tools are and also reveal some of the best data ingestion tools to help you take your data strategy to another level.

What are Data Ingestion Tools

In the last few years, more businesses have come to understand the importance of data as the most realistic way of gaining business intelligence. Thus, data ingestion has gotten more popular. However, manually extracting business data from a vast amount of sources has a lot of challenges, with negative effects on time and finance.

But this has become a thing of the past with the advent of data ingestion tools.

Data ingestion tools are software tools that automatically extract data from a wide range of data sources and facilitate the transfer of such data streams into a single storage location.

In addition to data extraction and transfer, data ingestion tools help with processing, modifying, and formatting the collected data to enable businesses to carry out analytics procedures more efficiently.

Benefits of Data Ingestion Tools for Businesses

Data ingestion tools serve many beneficial purposes for businesses at all levels. From time and energy conservation to better data-driven decisions, data ingestion tools are crucial to helping businesses grow.

Let’s take a look at some of their key benefits.

Faster Extraction and Data Delivery

Data ingestion tools help businesses extract data from its sources and ingest it into the required destination in a short time. This ability to save time and energy is vital to businesses as it improves productivity and, ultimately, revenue.

Scalability

Data ingestion tools are efficient for handling any volume of data for businesses at all levels. Growing businesses usually work with a minimal amount of data, while large-scale enterprises operate with larger volumes. Data ingestion tools remain performant even when overall data load increases, ensuring that the data pipeline does not fall behind.

Cost-Effectiveness

Data ingestion tools are cost-effective because, since they're automated, the high, recurrent costs of manual processes are eliminated. Businesses will now be able to save money and invest in other productive areas.

Click the image below, to compare the costs of building a data ingestion tool from scratch and buying one from a trusted vendor.

Short Learning Curve for Unskilled Users

Data ingestion tools are built with user-friendly interfaces, mostly featuring drag-and-drop functionalities for adding and removing data sources as well as selecting data destinations. This doesn't take too long to master and thus eliminates the need for expert knowledge and long training sessions.

Accelerate Customer Onboarding

Faster data ingestion results in accelerated customer onboarding. Whether you’re using data for a SaaS service or something else, the ability to process new data in real-time is key to ensuring that new customers get value as soon as possible. Data ingestion tools are instrumental in this regard.

More Leads and More Sales

When a business can quickly transform inbound data, it leads to faster decision-making ability. Data ingestion tools help businesses capture and transform data used for generating leads and sales in real-time.

Better Data Management

With data ingestion tools, businesses can manage data more efficiently. This leads to fewer inaccuracies, reduced redundancies, and overall improved data quality.

Data Profiling and Cleansing

Data ingestion tools provide a set of advanced profiling and cleansing functionalities for simplifying data transformation processes. This is helpful for data analysts as it saves them a lot of time.

Improved Decision-Making Ability

When a business is able to extract and transform data faster and more efficiently, the decision-making ability of its staff members is generally improved.

Enables Faster Data Transformation

Data ingestion tools eliminate the need for batch processing because they can capture data in real-time. With real-time data ingestion, data can be enriched, normalized, and filtered as soon as it hits the ingestion layer.

Top 16 Data Ingestion Tools

Having discussed what data ingestion tools are and how they can benefit your business, the next natural thing to do would be to look at some of the best data ingestion tools you can try.

Good data ingestion tools should be secured, scalable, support multiple data sources, and be easy to use.

That said, we’ve compiled some of the most impressive data ingestion tools helping businesses grow.

Let’s take a look at them below.

1. Improvado

Improvado is a full-cycle data ingestion tool used specifically for marketing purposes. The system automates all repetitive data operations to take the burden off marketing analysts and let them concentrate on their primary tasks.

The platform streamlines data from 500+ marketing data sources. It also provides pre-made data extraction patterns that allow marketers to start extracting data right after the platform integration.

Then, siloed data can be transformed with an MCDM normalization framework based on custom metrics or parameters and ingested into a data warehouse. Improvado ingests data in batches with a one-hour data synchronization frequency.

After the system has normalized and arranged all data inside the warehouse, it can push data to any business intelligence tool. A visualization is a vital tool when it comes to analyzing marketing performance and tracking changes in marketing metrics. Automated marketing reports are available to each employee across the company, thus improving collaboration between different departments.

2. Apache Kafka

Apache Kafka is an Apache-licensed open-source big data ingestion software used for high-performance data pipelines, streaming analytics, data integration, and more.

The platform is recognized for its high throughput and low latency. It can deliver data at network limited throughput using a group of machines with latencies reaching as low as 2ms.

Apache Kafka is written in Scala and Java and can connect to external systems for data import and export using Kafka Connect. Because it is open-source, the platform has a vast ecosystem of community-driven tools to help users get extra functionalities.

3. Apache NiFi

Apache NiFi is specifically developed to automate the flow of big data between software systems. Leveraging the ETL concept, Apache NiFi is based on the “NiagaraFiles” software project by the US National Security Agency (NSA).

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NiFi offers high throughput, low latency, loss tolerance, and guaranteed delivery.

The data ingestion engine runs on schema-less processing technology. This means that each NiFi processor is responsible for interpreting the content of the data delivered to it. So, if processor A understands data format A and processor B only understands format B, you would need to convert data format A to format B in order to make processor B operate on it and vice-versa.

NiFi is designed with the ability to perform as asn individual tool or as a cluster using its own in-built clustering system.

4. Wavefront

Wavefront is a cloud-hosted, high-performance streaming analytics service for ingesting, storing, visualizing, and monitoring all forms of metric data. The platform is impressive for its ability to scale to very high query loads and data ingestion rates, hitting millions of data points per second.

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It allows users to collect data from over 200 sources and services, including DevOps tools, cloud service providers, big data services, and more.

Wavefront allows users to view data in custom dashboards, get alerts on problem values, and perform functions such as anomaly detection and forecasting.

5. Funnel

Funnel is a cloud-hosted ETL platform specially designed for marketers. Its data connectors allow users to collect data from over 500 data sources for cleaning, grouping, and mapping.

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Funnel also supports an impressive number of data destinations, including reporting tools and data warehouses such as Google Analytics, Data Studio, BigQuery, and Amazon S3.

📝 Funnel.io is a data ingestion tools with its own drawbacks and benefits. Learn all of its alternatives with our comprehensive list. 📝

The platform also stores historical data, allowing users to get access to their raw, foundational data. For data transformation, the platform allows users to use standard and custom rules to map, tag, or segment their data.

6. Precisely Connect

Formerly known as Syncsort, Precisely Connect specializes in data integration via batch and real-time ingestion for machine learning, data migration, and advanced analytics.

The platform allows users to access complex enterprise data from various sources and destinations for both ELT and CDC purposes. Its sources and target destinations encompass mainframe data, Relational Database Management Systems, data warehouses, big data services, streaming platforms, and more.

Connect’s real-time data replication ensures that businesses can update data changes in analytics tools and data warehouses as they happen. The platform also automatically scales to suit growing volumes of data and usage demands.

7. Talend

Talend provides a unified service known as Talend Data Fabric. This service allows users to pull data from over 1000 data sources and connect them to any destination (data warehouse, database, or cloud service). Some of the data warehouses and cloud services it supports include Amazon Web Services, Google Cloud Platform, Microsoft Azure, Snowflake, and Databricks.

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It also lets users build reusable and scalable pipelines using a drag-and-drop mechanism. The platform also offers data quality services for error detection and correction.

Its ability to help enterprise users manage larger data sets, whether in the cloud or on-premise, makes it a good choice for large businesses.

8. Xplenty

Xplenty is a low-code, drag-and-drop ETL and data integration platform that helps businesses ingest data from their data source to their chosen data warehouse or data lake. It provides over 100 data connectors to popular data sources, including Salesforce, Marketo, NetSuite, Hubspot, Zendesk, and more.

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The platform boasts of letting users manage their data with zero maintenance responsibilities and also allowing businesses to scale to millions of data per minute with zero latency.

As an ETL platform, Xplenty doesn't just end with data ingestion. It also provides data transformation functionalities, helping users to process raw data without the need for coding knowledge.

9. Apache Flume

Apache Flume is among Apache’s big data ingestion tools, just like Kafka. It is mainly designed for the ingestion of data into a Hadoop Distributed File System (HDFS).

The tool extracts, aggregates, and loads high volumes of streaming data from different data sources onto HDFS. Apache Flume, while majorly used for loading log data to Hadoop, also supports other frameworks, like Hbase and Solr.

Apache Flume is impressive for its simplicity, robustness, and fault tolerance with tunable reliability mechanisms. It also provides several failover and recovery functionalities.

10. Amazon Kinesis

Amazon Kinesis is a fully managed, cloud-hosted data service that lets businesses extract, process, and analyze data streams in real-time.

The platform is capable of capturing, processing, and storing both video (using Kinesis Video Streams) and data streams (using Kinesis Data Streams).

Amazon Kinesis captures and operates on terabytes of data per hour from hundreds of thousands of data sources and loads them to AWS data stores using the Kinesis Data Firehose.

11. Apache Gobblin

Apache Gobblin is a distributed data ingestion framework for the extraction, transformation, and loading of large data volumes, from several data sources onto HDFS.

Gobblin handles routine tasks required for data ingestion ETLs, including task partitioning, error correction, data quality management, etc.

Gobblin ingests data from different sources within the same execution framework and also manages all the metadata of these different sources in one place. These features, along with its scalability, fault tolerance, data model evolution, and more, make Gobblin an impressive data ingestion tool.

12. Adverity

Adverity is a data analytics platform designed to serve marketing teams. The platform provides automated data integration from over 600 data sources and also lets users visualize data streams from a centralized dashboard.

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Its advanced schema mapping functionality makes it easy to fetch similar metrics from distinct data sources to help users achieve a consistent data structure for their reporting and analytics needs.

Adverity provides data security through international data protection standards such as the EU General Data Protection Regulation (GDPR).

13. Apache Storm

Apache Storm is an open-source distributed data ingestion framework written in Clojure and Java programming languages. While it’s written predominantly with Clojure, the platform is compatible with any programming language.

Storm has the capability of processing 1 million tuples on each node per second. Along with this, the platform is also scalable, fault-tolerant, and offers guaranteed delivery.

Furthermore, the platform integrates with any queuing database technology.

14. Apache Sqoop

Apache Sqoop is a command-line-based real-time data ingestion tool designed for transferring data streams between Apache Hadoop, relational databases, and other structured data stores.

The platform got its name from the combination of SQL and Hadoop (SQL+Hadoop).

Sqoop uses YARN framework for the importation and exportation of data. This provides a level of fault tolerance for Sqoop’s users.

Some of its features include parallel import and export, full and incremental load, Kerberos security integration, and connectors for all major RDBMSs.

15. Dropbase

Dropbase is a database service for the transformation of offline data into live databases in real-time. With a vast variety of processing procedures, the platform allows users to quickly carry out data ingestion, transformation, and loading tasks.

Users can clean and process data from Excel, CSVs, and flat JSON files and then load it onto a Postgres database. It provides a team workspace to allow users to collaborate with teams by granting different levels of access to data projects.

16. AirByte

AirByte is an open-source system designed to help businesses launch integration pipelines in a short amount of time.

The platform gives access to over 120 data connectors with a CDK for building custom connectors.

Furthermore, the platform provides log-based incremental replication functionalities to help users ensure that their data is up to date. It also provides access to raw data (for engineers) and normalized data (for analysts). Users are also allowed to perform custom data transformation using any dbt transformation model of their choice.

Accelerate Your Data Operations With the Right Tool

Data ingestion tools are an integral part of a healthy data ecosystem. With an automated data flow you can open previously overlooked opportunities and get a fresh perspective on your data. Data ingestion allows analysts to spend less time on routine data operations and concentrate on analysis.

Improvado can help you fulfill all your marketing data ingestion needs. Our solution streamlines marketing insights from a variety of marketing connectors and ingests them in a data warehouse. We automate all repetitive data manipulations to give marketing analysts more time to research for new trends and analyze collected information.